{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clear previously loaded data.\n",
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wE/0T/XZKPdK/Wy9HbOy19X0XU6pUtJl+lc0dfU6aJpxYUNkzHUCrb7LVQTfT9tRmppkP\ndK52Gj06bX2/HAkDkwG/96dWb9nHf/ILH3NZNpL1wzkYJDGDxCoVpBmhyY8g9Ognune7vR6pyZg4\nThg+Rs+O4fyO1sIgSWn3dXwyl+ESk8EiDNfSII7zNni+nz/fOZd/J05iBn1t28f/9T+8ZR//9l//\nUy6X8eLT7bYA+NinvsrUsQ8C8OR7nmHzqp4Pl86+Sr+v+6PggWcd83yPalXXWL/XYdDXcUicsN1o\nW9tCZpeP6/jEHg8d1rn4jm+5n7wNjNZoljrSsfU/7HuWZDg7i/7y3/jbt+zjxDM1wQQTTDDBBBNM\n8DZwRz1T4hzO6rvJuAWEG/NAjX3/mvduHCivHhmzelKPQqYei8Vgh1Ko2m9zZ520tAjAqWNTnDis\nlsi5tV2cvJk+6d7k9c3h+T6+uaA8T/CHFrAIoW9Woe8RmoUY+T6RWca+jGm3AgS+/XUhtaHwRAg9\nbY+Pw4kb+4G9EhB7kue5XLv2nODblVKSZvrF2+xhVOtTLOn4dfvQ39Nf9hspLtPlNGiklGfUy5DE\nMa1tveqrtDBPsW411BoD0rg77NSo7Rm52SvOy5uYik+zoZ4RLylQGXo6ohK+jdPyIGFxUdvW8xLS\nVL8TBjA3O7vPHkK1WqVj1nYYhnkbnMvyNRlFUe6linyPqYJZS4MBA7PCRTwIrJ1+kK8LJEJs3rK6\nYzCnN1NM16bpdNXy22h2WJjSZ86WYTPTvzXjQylQj6sMWhyu17UNpULuJdkPTp46SndbLb/luQVO\nPqCekdjbw7e6fQ+ceoDDW1rP9pPPPU+7pRatT0jS0328tdnjyCn9fpQMWNnTcZ4+Ns/sQX3d2N2h\n31WrcWG+QqWma8AB5bK2uYQwZZ6+FKFUV68AXp+CjW0vdnT641f93Ry+7+dz5415np2Qz50vHsJw\nXwrOvLilwOfYQb2p4snHH2T98nkAXvzNTzB9SMsqFcIAz+a0ubvJZz+udVhf+OwnqUW6F979LR9k\n6T71GH7lk79Ke2cDgNAPEfMYeEDBvNk+LveE3gpZSr6HPU9IEt1zDvXk6rNd7hH3PUHGPNnjuKFn\nCqdeV9TbxZg3byiPdlZWOfPSywDUKhWOP6wMQFCrkZqs9/FIzQPisozbSXp6/eouqe3F3TijtGGe\n+1aJ9q6u/epGxtSsvk6BaF6/32n1ae3oXinVevQ6Km8utjP2El2DYRbiNdW74QXZiBkIqpze1POj\n4hJ2++YtL9ZJBrreCztd6qGxIplHz+RseZCRcKOr8G6MLM1yFkI89foADJIBmKz3PDfyQKUpnYH2\nJY5H72eZI7U1cO30Ogb2frvXyz8NPZ+BsQxpmhBbG7IxOZeNzVeapvlzXJbhbnBmvxkqSwdIzbtU\niEqIyfK+eFTNC91rrXH62d8E4NTROR59SNfS3OwUtbrK9XgQEyd6xgeBT6FQsDYn/OonPgPAxz72\neVLzoC0ce5irOyp7tloDFqZVlrhEWR7Q/zcxTSLg2fhkveRaD+stcGeVKTLE6cIVkZESdN2c3Eix\nuimGz3EB3Y4O9ObWRebrOqCnZo+ykl0F4OmTi3QH+n6SJPjhjYZgjPK7TXjiXdv+zDZhKkP2StUA\nZzPpBAm1/b6Az2hTDQU+AnF+QINvd1aqMPZGzxyDfIOuPYuSmIqnfz+NSjSMkgjbGYG1JfUy4oG5\nyyVlSLX4cZOwrpRQOmAk2BGG4+15TukF0LHLh9LRbug/DkQRdaOoDhdLpMOh7LdZ7ev8NwZ9Utst\n02nC1Nw1hXBviiRJ8oPJ87wxeo5rqRETDrO1Ah9+7+MAnDt/gVcu6oGZuCinQzKXMtR7xffomau9\ns7fJex89DsAH3/d+Pvaf9B7g1Y01SkZxeu0O7R2l1QpzIL72pR8UqE+pIraV9HHZ/gX42pUrFDxt\nf33+ML1gD4DDDy7QXFcasdncIe7pOC/OVNhqaRt8YirVGgDPP/saFw/qFV8nDy1QCvX2hunlA0ig\nh8LsTAmZ1r6UCiHOKOgwCOi1VNBlEjB7TJ9TqFdI7H7kMFSqGCBNO3jFW4cGDOGH4Rhb75DhuhpT\nEnzPyw0S3wvADtmgkFAx0RBWyiws6Lr93Be+zFqs7Z+p1ylX9bDeXb3Elz+rwrzmCw89rgWyj546\nzvqWroftqytUSzannuBSna/Q9wmjgrUsy/fLrSDi5cbpON3nieSyIxiTIypzh5TN6DnOufzwERnR\nfyJCt6djP+j0csq6EIZkRr2++tUXae/puki7MVfPqeE0e+okQy0uDEK8bEQVBf7+j51+V5CSGQxB\nG9/TeXC1++h19TnTlTLFzCiedIfYFNksbpOEqli144wEO0g9n4LJsACICqrIBqHL6eh23MHsF5pZ\ngaCsz2wNUtJsaKgK28bCiYPEFGLnRobcvpC5fJnGaUxi68K5LB/zJE3JxWKWMbxn2ZeUstGUWZbR\nsj0qCEPqMMlSMvQ5YeAjbkT/ZW6kQA1PkWv+1+nfBkjihBEdKdcuolug1U7wbd7LpQIdO6ez1CMq\n6bitXLnE8pLKle/9nm8lsGWS9GMuX9Lz+yvPf41zF1etbcKxo3pV5Ec+/H6Wl/T2piDy6dg4zM3W\naTS0zduNLg8c0+8MeileMJxHj6GgGDiY2twGoB8PWC/v30Cd0HwTTDDBBBNMMMEEbwN31DMFCeKp\nKi94jPTxt6vTmRtSUoKyWh9VN83m+usA1ApFjh5Ubf/YUsTnXhrW0RvzIrmv9+68NYx5MRhRbFkm\nw7htsozc+vDwCYz+8xByZ9TIWQM4PLMsZOx/Ecm9Hfq1IT3GyFs39v54JwUZepDVkt2nNZw2AxoW\n/J36AaEX2vOSUVAq5B65QMI86LXfGhC3d/NB8M2lLt6oE55zuDHbaOhB8MQhqVrvaaPLoKPraKpS\n42pPrc+VdptBS13zURDmVGd3d5OCUUj7g8MfUrJhiGeeKZelOSXk+x6etfPA4gwnD6rFUy9GtHo6\ntytbbVLzQIrnE5gHstnqsX5VL68/uVzjuz74bgCOHFriKwfU0+G/6MjMne2RMu/r/M+EHt2BBV2m\nKWt7Op6DKGCxtH/vWyUqMr+ge8WrplxpGBVbKdDz9O+GWYqY5+PBEwfZbKm3cWdvh7lFpc2LhZDP\nPKcB9IO4xH2Hte9Tfo++eQ/TuE8Sqydus9XLky8GccKetb8Vw1JH5/ddH34vGTqPg6xDP7WkgqLH\noL9/z1QQ+vkacM7l3lq1Q23PCbnVLoy8pRkQm1dm5+IFVtfVu3Rxu8Gz//7/AzQBZM48g5l4NAf6\n/G4kiK23M889x3nz1px84BGOP6h3srvMIeZtDgshnpnhqWNfiTnapyyn6wLfx1miTJZlI8nqebln\nyjk3knFjCSuCw8W6nxqNBp22jv32lXXaLd1bWTpKiOl2uhQrutY2t3aoDWnYnT3O2piFB5apGgXt\nic/QGZW5LPfQ7wczdY/EOhm3ilRq6vHbaRSphUYbZR5kum/KxQVaibZ/vb1CVlE6qeBimkbnzRYj\nZgKjWKeEju2zQb9LoajPnC4FpBa8PkglD0uJfBmKNsIoI7V9iUvIrGORl74J43FjZDhiOw/iLM69\njOJ5ZDn15vI1G4VB/p1UYGZGvTntVgsjChgM+uS0NmNsj0hOt8VJks/peKjNOMucpOkoEUpGz0mS\n9Lbo2vFn9gZ9tnbU++3EIzKqrt8TyiXdT812n4U5lTH9bputLZ3TYlBiyUJFet0enV31Ir3y4qv4\nUWTjI/S6HftthzDU51+6ssn7n1DKvVwpkA13g3OjufMjiuk6AGGSsleu7buPd1aZ8hL6K18CoIBH\nNP80AL2wiq1bomykjmRjE6BLafhGxo3iqTyX4UyIzR+YZ7FmGVO7bfz2eQDS/hPs7qlgL6arhMY9\nJ1kRFy7a0wJSGxoVS/sX4LhRK8VzDLWdDJcrLM6DzCiWzIOBuYSzzMPZJg89/1rlbtw9n/fdz98X\nN+K29U/6Yz8bZYoMhYIHY/FcHtk+4zQGuz5xYrEQflu5Sfs7I4VspOT54uW8c5plOIsliKKAZJiF\nmUmuNCEp+GOHW34qOCKbhu29XbKybrpBIaCXWpxDoUDRKNCSH+RxBXv9Lqu9nX31D8DzVHkDcJ4j\nHcac4eVu7ixJyKydpVqdekUztlInPHbfUQC63dNsbOumTvHz7MWttV3mTOn/yAefYdkUE98vMTul\ncUZBWMqFfFLwmJ/X7/T8Ps20nbenYVlDUVRg4/LKvvtYK4RULVuwk7U4c0WVu8z3ydp6sJ6q1njg\nqPZlT3Y5flLb8GC6wLuMkqvg6K4aLbiTcaanisNcZYOqxdZ12m26ln3U3mrQi3WuGy6g2bZMsCBg\npfk57cssPPQejdUa9DMqFb0buduJKRf2L7JKUZivw/Q6Y2mkvzu8YXyhCIW60W0uY6Ojc33l+dcI\nbV09fWqJT76qGY7ztQLvPanz9fpqC7ErjdMk5syqrrepjS2ae6qEfvD7f5Da/AEd57hPaLKqUK5r\nfB0gWbZv09LzwbM94eONkpE9IXNDqsiRpSMKL7V4n36rw9qa0iWDbo+tq3qAbK1u5Nl5vUGfyA4T\n50UkyUiOpBvWv5k6ntFk/biTZ/SSCpE/ynbNvOFevza77Fbo9xM6RonPLDxMWNPYvq1zrzFb1LVw\n5uIV4sFQ6b+PmZq2ec9fYq+h35HKBmHRYm0KCZbgiCdQGmbfJj5YzK3zoGD9ot/PleyCZDlFj4wy\nJZ0LkWxIp7p9G6egNFySDccnGylTbkTfep5H4A1lbUYaaztj4MqaKvqtdovU5sg5R2jKdZqNYp0G\n+RxybeDcdbRvNhYnlWe3jVPJnjeWbX5rRKEwzHKvVMojGjqK8pg+lya89Kpm8507v87RZaXwlpen\nOHFQleVTh+fweUL73m2xvn4JgDNrVzizNmynD6Lj09rbYWFJZdj6xip7Fjd3eGmRvu0FkVH/BwjO\njFLxikSFCc03wQQTTDDBBBNMcEdwRz1T3mCL9OLHAShFAeZ9w9UfoRcoTZI5h4hF9TkfYUhXJSP3\n99dl4FkwqRNSp9pvt9lkvmIBy3M1Wh3VYJ//8m/z6tfUCqPXpxyqVT23dIQ9NEtnJ1ki89XidHi5\n92dfffTHarO4EVd3jZ1ybfmWUSYNGcO4RXEObxhxKMNspGuD8911tXNG9B95LZcb1aEZYmhteZ7j\nWrv9Jhj4eRZS7FK8xIJ3fcnnxWPkafIgD7b3MsfhE7rkDp6ssHpVPSzttrC3Y3WxBpJbTOKP2pX5\nQsFqOSW+j18xi7lQpGSDFovQ7dp30g5SUOvEDzK2Bq399Q8b+yH1k7l8ubksIzGKLetndAdqwXd7\nA8o1tZxcMWDZKJP7tvfoN84A0Gg26RnVdXCmxvueUbrn/pMnqE+rV8vzShTNmq8GIUXzUga+T2Z0\najNJiAO1nMpTRYq1urWhy/be3r77WCr5rFtmWbVUZjDQsdrYbVG3oMvd1i4vvGQZlHM+iwvax+Wo\nwIxZ8FdeO0PZLM69TpE3NtULUyl6nDyse7pUKNCKdazidpvtlo7hq2vbDMw6XJqeIltX6/nw6zXu\nf0wDjbPUJ3cEJCGlYrTvPkaBny9rL4Ox7TRGg0lO44rINZTu6orRl9QoFlQOta7u4Rln5Xyfiw39\n/k5cplbRtvW6Db78inroHjkyy9Pv+XYATjzyDM7WcJYlefCvN8bVi3P7Duwdp+3GPQieN8pQHM/C\nW714mfMva+hDa6eVZ2912m32LOhWwiinnKJCRGL7Ox70ci9MsRwSiL4u4NFpNGzMHOVI18LulStU\njHZxU5VrrPabyaTrEQaOufIpAKYXH+fcRaWFAxeztqNtvrS7SceyS1txzLFF9Wp6OEqWKOG6KZF5\ng4NSl8y3gPXM5RmZYTUis4zkbrdFPKTwxBGFw5TIFM/X8fEJCd2Qv/TpD2VumhCGY2EWt0CSJTiT\nN5lzpMMsT6dZnwBOMnqW5Rf3B/kcdQYpvY5ukEIY5HRxnDh6sX7n+KGDbDd1jq6srRLa+lXv0JDx\nyPKwmyx78/U3zBwcr4e1HxxYXKBgXp5CKaRk9cgKxRrOvKiDfp/GrsqbqzubnH3jDQCefPg4jyx/\nAIByWMo2c3hlAAAgAElEQVTrJvpFn1JRQwBmKykvtdXT2k8yzClHu7HLIavhtuuEsxfVq3zi+Akk\nGGYmujzztJQKzJlXPEkp3AZde0eVqbn+BWo1FarlKCXe/X8BiLKrdGsqcJqFw8Ri2RWOXLD4kuaC\nw13nUMtTND1BLE2+FBRx6ALa6PToxSrYz77w27TW9YDr7HpIqAvx/sUEz9O2XW55hNN2ODrBY/8b\nw/NSPBM6IqN4Bc/zct5YhJFbX0b0pYiX87hJRh4/5Y0RnG5MEIkI2TAm5zoFM7sma2eU9jz6W2O+\nzduIFnMFIbHYkISMYSTSKDoFAjyCYbo5HsnQZewJSTKMIYLlIxYDRYktO0gvvtbIlS/JXN7fovj4\nll6dRSESmTtbHJnx4+3VdTq7urnqpRLTJd280bTPntt/ppuQ4Q/d60mSZ30kSUxsfysd9OkZjdje\naeCbEJPMz4tAHlmYpWmHVK8+Raevvy2UixxZ1qy3YligULSigRISWDsXpypUynbYZinZML6mXGd3\nQ42B7V6DdHfP2jYgqtT33cesIHR6Vjoi7lEcFpQdxFSM/mt3GrTsO/0OeL4KusMP34+zPbF45EFC\nE+ztvSa7HRVWlzdaNDv6/sHZKr7Ft1QrEccPqrCqLldZvaxu/dmiR7unzyxmDpo21zFkdigUCwWS\n9mDffUS8XOAHvuSxeyJeLjMELRmg748UeYfkVEen32K3ZwfNIKWIyoxytw8W7+H5BXptlTezQcIl\nUzDqy+/lD/3IXwSgMjtPY0ONOhzIsNCrL+CGcSmjmMFbwfM85c0wCn3sEByKAx+fyxaz9du/9Tm2\n17ft/YxK2cqIdPtkdsBGnlAOdd+Up+p5mMJ6v0XPsqAX5kIiU5p2mjGNHV0X9YKPMyXr/NkzDGyv\n3P+ux3HDLEbxuB1CpFAKWVhSw+PyTpnTr70KQJD2aNhaa3baRJbxt7G3TdLWfVkrlDl2VA3kqfoJ\nGg2lwVupTxpZ7E+6S3VKlfswBDe0470sL/8AAYnFoGb9EoO+KmilgsMPLH4x6xEVhwWXM5D2vvvo\nxkI0vq5MkL0uFKLRqA1S2nmGa0alqMbV/Ow0LYtrbA+6LB3SQrOPPfYoz7/8EgAra2v589M0HdF5\nLmMUzCZj8XSjDFDN9NRvJMn+KT6AerWWGyHFUkjZQgDKxQqZKez9Xp/Azu9iuUxm6knoR9TrWqak\nXqnQtT7GXsD8jFKBO42UQaLz6/yA1MIKWs0maaaTGhYLvHFWwxk+8O4nicwITNN0LH5byMJR+ZD9\nlimBCc03wQQTTDDBBBNM8LZwRz1T9f7rSEX1t1avTdhR7b3gfZHAqUchq3+EbkHduk4EZ4HaqfPz\nQNHraygN68RknpCZOZE6CEP1LmUOzq+oBd/rrLJQVUu3uwCbFrT20gWP+lG99qRQmSIdut4d4PY/\nTN1ei3pNKYrrK+ONvEcj6zPLslFhPFwedZ+JkFkwZ7PRYKaq1kcUjAoRijeyBHv9xDI4yJ8LEIbR\ntXVmcu+YjFGNcn1T37x/lQHx8Jd98IceMM/LHVy+g3AY1MvISl5f6fH6C2rFvv5ijSc/qFTq7FGP\nmUW19lYu9Bhec4Ln5R62GQkIY7MCw5S9VK1ScQIdtbYLkjB1UIOkD80vURkWdCNm2qyT/SAd9HAW\n4JklkJlbP81SnHlJQk+IiuqqLjlH0tO1nKRdMmtnlsaUitqGqakKA6PG4nTAwLxUvvgEw2yleJBn\n8FUKHiVzF9WLFUJzi3fjPrI3vDKnS2b03HR9irC8f89UNFUkscyfrc1dZivqKWuHPo1Vbb+fCKVA\n/66EAYeX9DsPn7qfOat1FXt19q6cBWB7+2USoyO7XolWqu3faraplrWPS0fnefp9WpMrywa8/Kwt\nmm7Gyuqe/d0EZ23rN1Nio0f7wYDYCr0e2kcf/TAceYNh5JliVEsJb1SkTxh5uT1PCC2sYDYY0Bad\nx0EQcb/Vxbl/WvKMSFet0doxS1pipHQcgPd99DspVXVeeu1Wvtedy/D8YX2j4nVBvvvzTIlI7vXN\nuDZw3Tfv+MbVNb7wWxrYv7GyQcHWLIMuYjIi6Q+QcOg1KDN/QIPko3JEKdI5LJQatFq61iJfuLRq\nHtdU2GjaFTxdjwfn1YPgfNjdVJp0/fI0hx560MZ4XO7cGonns9HSNpxeaXFxV585HTRZWtAeHz+x\nyI4lCTe3RVNDAZfEzFhG4fL0NNOpjvfq1mWaTaOZwiJStHOoGOfrJfXLJBYy0ut4dFrm4cwqlEMr\nWtztMrA6Vk4CIgsrKBQLxGln330cDOJR/SbPjeQ15B6oSmWGEzYvr71xhrbNl/iO+WmVo8ePHeX1\n118DYL5W56BlGJ89+wYba1qnyR8b/DiJ6dkZ44RcDgmj8yNOkmuuXLvRFUT7QVQs594fLwhxtj7D\nsEBqyQPtVjPPSC0WZ3MP0aWVLf7zZ54H4KPvf5i0oyEb/UZ7FKD/8nmesGQfCQtcNDndbXTy7NRy\nscLGlnr119e3OLisZ8Vg0M/H3+Hwh1mQfnA7ET53Vpnaa61RHQzjZ8o4i7in36HIaQBSlyBTOvFJ\n9BgxSgn0/R6+lVUI3fVp7kMXaZorF52eEKRWkbuf0U91E0oQ0+lZ4beZw1ROaVbSVvEE7eCYPS/K\nxZlWE98/Ll85zyMPP2XtkVHsknN5OYTQ8xiFOrlRIT2PPEZsa2uH8xeUAmk2WyzOaEbTgYU5pmdU\nSQwCD0zZvHDpcr64a5US3a4Vhez2qFU1861aLlO2lGbnslzJ0fTp/fWyTxPP6IHIK0BvmDUkeTE4\nbyzSTTzoWRmDi6c32NzQA3P9cjFPiX1qusjsvM5VpVaiOYxLco7IMp6ibkLa0ffLyzO8sa1js5Bu\ncbSo3ykfWKRbVGGeliqEFidVrpVJBvsXbt1Oh0F+31hGzwSj+IIYHRNFIQWLtZgthrQs/qiVthi0\nTSmIQmpzKug6vRhPlAaoV2qEljGytblF6A2LCXoUjA6pVArMHqjb96dJTPmKXMCTi0/qXCRxXlSz\nXp+FsLjvPmZeQmJxTFnseOUNpZ8CaviibasUi+xta7+mpsp867uf0e8nCXsmhBdPHKFW0Hnf2lil\nf0H3rlcqQMFKIHgDnGXhVebmqE7pWl5fOU+joXOUtmP6w7iqeIAzqrdamcmpiytXzufVjx/fRx9L\n5RKp7blxWsL3fQI/X/z4JsADPyC1+a3UYmZntZ3BoMfV1jCDNaKDrueu71OyjLKDCxHtSNv2tbM7\nTC8dB2B+aYmu5auLjDJDRTxCSwP3gzCns/WQ2h+FMl6dWpC8uKwnXp6tePb066xYlmelVKLsD+ML\nffqW2eQXI0qm2B1YWBwdpLs7JLam9hpdSiXdr9utAV96Q+WplyYMBiprqodmma7odwq1CgwPtG53\nlGnoRuVi9oNGO2F7TWNnLq95RGU1JE4ei1ie0ueUo4yiGb9pA6JpHdcojPCHWdPxNs7WwN5Gg5m6\nyVCvTsNo86TXJbYQhr0O7LasOHJWJrBioVPlKtWKlRSJfDYtNR9vABZWknndnDreD9I0yY3GMAjy\nOY3jmGSYPZymFEvDTPU5mqv6/bL4HDalqd/vaaFa4KlnnmLO9tnFT32KQ/adSrXOpWHmbpLlcYRJ\nmpLZGHrZKJvPMXaHXZaOFSF2txX7Jn6Yn6mDzNGw2xRcENCz0jatVpOBybO40+VJo01/TxawtqFr\ndXOvhd9Sg/zC2ibtF1R5fP+Zq3zICrFejQp8wcqvvDho0zPjc2Z6hvae7ter63sct4KfcRznFr/o\nvSKAGiTDjNT9YELzTTDBBBNMMMEEE7wN3FHP1G7hIN1N1YpLQUZoNEacBgRWjK/oXoTUPFOFLnFF\no/jTUiEvrue+jnYbVsMUMruCI/Om6IsVCmxfopvq67B+kDRUl7Nfewqpq+fAj8r0M7NQU8bqLmXI\nbdSZOnfuNPfdr3cK4ZcITONttTtcuKCB7ydPHc/vinNkODMjO7022ztqqW/tttluWmBeUqG1rtbw\nhY01fF8zEMPQY25WLcFEPM6ftxo/1ZCBBfV1BkIpVB94PfR58EHNbChUfLK8/lRAwwJmb4XpMMML\njQIr1+k3zNvWG+R1TcQbC1okYGvdankNyGuNuD7M98zNetGn46v1cOr+gN4h9YxcOtcl7GgwrOeE\nRk2fGR04gBgV2NvdZN6Cg/s4uual6sYpXknHYGo5YLCx/6XuyYhejLPBMMaXOE5zGsATwZwe7HS6\ntLrqjeq0twiGVmatSmQ1ktrdHs6yVMOoyFxd349bDTY31XNQrNaZWVDP2uNPFPDCodvdw8V2N1WW\nEltGTblQxFlWY5aOaKz9YGXlIrN2T1W/FbE5pEGDjMysw+m5Ogdn1HO7tDjH8cNKM7zy3FepVdTS\nnQ0LFMwDVZ0q57W3er0e8TCLsxhx9IH79TlHT3D2vMqAlQsXuLphgbqDlEx0PRbrVXyzSpNBRsH6\nWKzU8jsQ94NCMcKZFztJM7zcKzSefDF+bZMQ+NaX2nR+lcfO5SvMm/etUg4oRcPEiQHd3PvSJ7VA\n4E1XoL6oVu+BpWUCo2SSfntUkNaleRuCMLom+9bdJJvqWvh5gk4Yju76S7OUblPHdeXKGt22yr7p\nSglna6TR7bNl3FihXKJsruRmo5HTf8sL06w1dV30/CLLw2D7MGRYYqjZjSmZ22l2pk7FvCFpKrS7\nKlOW5k/l16u4bJQcsx8Muj6zZd1bDyyCczo/85WEqq2F3UabwNZgVMjoWn2wSl1IbH52koTm8Oor\nILHiwe1GRtM8dJ7n8vsz49QRhLrupmdm8tCNvd0d2sau1CqzTM/rPCeDAe2ByuXU32KqsH9PuBf4\n+Z2pvueppwSdx2EtwN1mkzNX9Fw8tDBPbFmwSeYxN6v0+7mLF1la1lCV6dm53Et18MgxDiyaxz5O\n+dKXvwLA5tYW65Y80O4l+dpMXDa6QiZJEZPrTrimttT+ST7NjBvEw4QLj65RxriMbldl/6Dby//u\nY2HEfx/qfrovbvK8UcYv7zyIt3QfAK1GxtKyvi7PHKC/rnL0+MY2j9uWfn6mwv/Z1zM1zlJKRnP/\n6q9/hpdO63n8vmce5eQxDRwYJo+Beo+D2/BM3VFlyi1+gKyhLtVe+5U8myWSFBdYrFPPUbKDSUqf\nouNpumPqfQf9UAV7RjrGvbmcXkIyqhZbcuzQw6yuXwbg0t4afvn9AHgLj5PUjusvvSkSoxq9xFG2\nGI947M4rkWyUabMPXLlyiVZbF8d0oZZnHl68eJ7P2N1dxXLEvClTGQF7Fveyvt1GPF1AWTFip6dC\nsDdIKWa6OauFkH5fDzsX+sTq0Wat0eb8qsYRtWbreXZZqw89ExwL1SJb5pJ/4MHjzFpafbvd5bOf\n/Q0A/uQfeO9N+1dO60SmBPciiO2wKvoRxcGwqvuoWKnLhL1dK9jY6RFbRt6JA8s8vaAL+OLuJtur\nOgYnHw6pTqnQbmxmuKaO30anRcEold3Tp5mxcaqGNbCq562kR7Nh9+KFEZFlhYZ7uzTTqZv2axyl\nYpHY6NAUIetb3F5/5NruDpL89SsXLvOePR37mpcQmZCPfY+lJaWpy5Ua3Z7RAHi0TIgFkZ9fbLrT\nbNI1KmLhwDJ5BWOX5Mpplo0uRc2yjISR0Ls+y/VmqJdniKwC/dWtHXzf0pYLEb7oPC7MVKkZtfDI\nQw8wO6XUSNJP2M3s7kXP0bC061Zrk7qVBxhkIaFl7By5/yj3P66F9jaunuXCG6r0b2xvsrptcXCd\nhIPL+v0wKOcFEHFpbpAcOXyUONl/Vmbgj6rvB250H994VXDP86+hK4YVp2PnwARp34/oNe2uQAY8\nuqRGwNxMifkF3ceV8hSvbKkCc6ZxlaeNCq9WyrkCniVpXjQ3GfRIk2HRwIxkmKnqsvzOx1tB8HIl\nbDBI8qyldmOPF7/4HAArF69Qq+ra78cZl9b1UCoGRWbntSRHv9um2x9eritMWZxRO/UYWNjEsaOH\niYZVALKMxWl95oW9NmW7qHbpyDE8W0dvvHGahWUdm9LcHIN4WAbidoImwPcd9ZLOebUU0U10TuJB\nB6xEyMDFVKf0/eOnSnzlC1Y+YX6WwMqLbG7t4kdWGqFQRDKjx4sFxDJo4zjGH2bNliLKdo9orVrJ\nDc/ddoOupfwN/DAvP5AmGdOzamy0+l1mb+NOtyAYVTTvJ4P8nkQ/CAiDUVX77V2Vc3P1aZantW0D\nGVHW4hzLCxbvFoa0mqbQuYCOlSNZWpjj6Sc1TKDb6/K5L38ZgNNnzoyq8GcjOZekaZ4xqnc7jlSo\n/WadAkSlKjb8vPrGRTa7tv+ikK5lfQ7iAVOmzP6JoMDxTM+qMyceInhEif1ipc4lc5h8OIawpm1u\nLy1z7qETAFz66tcoWFmFrWgUUtPrdojMEFpbW+XZL+seuXD2In/sh/5LAA4szI7iF1MP39+/vJnQ\nfBNMMMEEE0wwwQRvA3fUM5VF98HJ7wWgv32IbOerALjeCiVnwbBBmYFZB6V0lWmjn7xORmNK640k\nhUeIPaM3/A6+FVqLgi7fckytlXcff5Jf/rxa0rK8TLmu7s9+WCEZWqXZKHvDw8sz+Jw4/KG3y3m5\na3w/2NjYYHVVPWKNVodZK+bYbO7RMOt2Z2eTnnmXUnxWLQjXry5QKKqr6fKZC8R2zUG1WiYc6DhE\noVCxQnRCRrGkFtPGa69Rqehvk6BCaMGiSdIlDtWyaGQud2N+7qunOWaUUpCmvHL62X31L2tVEbOG\nK9Mpsf1NPyoQGkVJEuf1spxjFAQ86BJZtmKj0+XT59TNWjlRZtru3du41KfZt6yhbontbQ3w3Clk\nzJqF0Wyt4zI1cyrVOr2hJe8LvhViy5KU3rZaNlfbMc1o/0vd9/1RRo3nE9jj0yTN39fsD21Qq9XM\n7zBLGZCkFlQ9VadoVxk89ODjebD4cy8/y8Ut9aDVfEfJqKVWv0NveLWQF5PbOpKOam95IWLf9xE8\nC7osAJ63f5d0p5Vx+pwG8V9Y20Xs+QvTU1RL+rpSKpCZt6NUm8UX9bbMzy2x21IvTL/fzAvKBl7G\nY0bnXdkecOCgejiOHF/g6pruiTdefpHVFR2H9WaTvnlk+s2UQ8d0r1QKNWKjpvADYgtQLRSLOH//\nezGMRpmsmRtd8SEi+Gbx+2N3OIZRMPL09Lp5TbF4kHDBMoUu7Aq7Jjbf9/ASSzNW6Dd2fO2C0gzr\nOy1ee00DY7c315mb0nGI4x6ZeeObe9vE5pGeml8kGtYac7Jvz1S/3yNJRtfG9Cxr6corp9m6oOPt\nZY7E6EovKlKxzMJqoYQb3gfXSfEsGH5mdp6S0XlhVMS3q4tae7ssLavXo9/pUrUCjO96/D5OnNQ1\n7oUFvvRFzboqzxZ58D3v0fclJB220/NyKm0/8ENHZh6KdqtB14YmkyIrm5aQUq7jB0rVTU17TM/o\n/PgE9MwbXKpVKVgtqjTuUbWtUvLBM89Uu53kd3JSKrG4rFR2IYrYMU9yKkJvmEHmB+ztKItydX2T\no85cL4Uy/d7+96JzbnRfXprmBXSjwKdg9bzCIMyLLG/s7rE8bckp9RKb5m1cX13L6b+F6RqZeQND\nT6hawkuaDBjWE51anKdi50Qh9PMrZ66PKx+n9kbVmOS2svkCl/Ls1zTJ7OOf/BLNnrazUCzT75q8\nzBIWrBbV8WzABfP6/XJYZKGvfTzs+UzN6Doszk5RsQzKwf33c/Xl8wD8241t2rF2orqb8IQlmS0u\nHEVMXi4f3GJ7za5Q2tig1bAs4QMj5gdxeXLFvvq472++A0hdRCdSYVueX8AFxwFobH6eZlNdbrVC\nm3JBF0qSVehZZVuXfZGwowIqKK4SzOrlsO3KNJ4J/MemQp4xAX51p8+lpi3E2fvoG+WTkOEZbefj\n5YXKHJBa5oeyVKNlczv5fI29XdaMux2srHBkWYVtoRjmlV5XVi7TGB5GCRSMEvDDkK1tpYvW11co\nFXQDhPRpG9+ceAV8OzT73RbVik7hkaU5SlOqHL3w+kVC25y765s4E16DrEelokJwb7fDy5taDTny\nevST5v46mAjDYuKVQKjN67P74kisLUE7xR8qBY78vsRECpSG1aO7TeKGLlovO0rS1XiDrJSxt6K/\nnel0aZg7vlSdJrWiqmGpgm/rPcAnbVpWj/Nx4bAkQ5GeUUWd7T3i2v7dtUPlDzQWJb8EMwxzwRLH\ncX4PVqlYoGlU42Zzh6MnlMfPanVWrWhnsTJNvaZK0IXLH6dYV4W4Gvqcf1XnISxP4Rf0fXW5Wxo9\nQpoNKT/J42TEE9zwAsssxfP3H4uyubvLtsXVpInHXF3XRSBJfsFou9UgsjV45NQjDGyfHT5ykrmO\nrpdkb5PGpq7ZXubll40WK/380tg3zpxme0PH4fKVTS6vWnxWKITBqKrzimV6brc2OZiqAAyjKuWq\nVdImIx3sv2hnVCyMKb+j+zwDz8czZcoLQopRKX9+d1f7snP2LBfPKB25u9kAO2haccxrq0bjhg2u\nDs4DMDc7y07XsoCCAhde0oPjN371Y1S+//v0/Sxm5YLKsHPPfpHESrtffeARlk5onOXsoROU6vuj\npN2Ygthpdzj9gha07G2s5PEprU47r3Q+HfmU7dqJpNcnSYaxOR7YAdJvN/CcrsHdRpOelZopRT7d\njo79XmOQ3wrx0KljpDbGLzz/EouLqpR94Du+LS/nkaVprggM52K/CKJinqUqpRIVy4jd3G3Rtznp\nxQMqVjxzdipladlKojRTAqMdZw4sg5X5cGlM0dm9l629PLvQ90NCi28rT0/THwxLmSQEptR4YcDA\nUvkllTwr8MLVNS6taizg1FyVucJt0JluRC8PBoNc8ZQgyO9VzDKXK7ndfo9tU9a66YDnvqhOiVdf\nfYM9O1dqlRJP2qW+jz54jIIZYOub61SsXEuWpvTMaJmqVtmzkgO9VveaW0aGzGyWZSNiz91ekYtO\np8OnP/d5AM6dfZ1CUdd4+eAJihZXeuDwA3mM6S/u7RLtqAK13ejg15Tma1UO0V7Xud6bPkZS0jNv\n/UyH06/rGbJw8GEefWzexsena/Fom80uM0bFVuce5NBxVbQ/+sHHObysz+n3e7nMSNLstu4fnNB8\nE0wwwQQTTDDBBG8Dd9QzhWSkmVoKA6+EN6NBdGlpCbeuGQm95pco9dWlSlTJLSOvvU2pp5pn4l+m\n3dYAM5a/n6mqenY+dLxKJVDvxa9ebrAWG9XkB1pFDr0l/M2z8+S6/38LXfSEK1eUPokzR2BacbVS\nzoPa+/0e21b7Ikkdgbk5N7c3ado9S0WEebu1POm36DaVFuo3MrZ39f3FxUX2drSP22srHLX7wwr9\nXWZKqoG7qVpet8mXCN+C2mtBAbErFa6unqVnxeFuhcj38lpLrhURhnYLvfTwLMLQTxxYQCuez8Mz\nGoj61LyjZnNyrBTx/t/3ewH4RJyx2R+5emuJWlG7jTUGlnFWcClJost1ypuFirmkBwGVWV1Hh2I4\nn6i1sZuUCQpqbQwGuxST/Xs0fN/PqQgRIbJMId+TnFYplQv0rJaXSzP2GjqfRw4eoljR9iwff4DF\nJfVMBoOUNNa2PfPYEzTbupYbq1fwrCbK9s4uMwcscy0IGdF8HnE2vPbB5cVfPREySwDI0gBk//SJ\nC0IttIrSczM13UMHFmpMG4UQRo6HH1NqfXZhid2rannPLyzx2itqNa5snOfsa1q0c3f7EqldF7SX\nZizs6RpsbO+xYTfbb27t0U20nbXpGg8/oB6odrTFuu2JV8++wcIRff/g4SX6Vl00IyO9jT6GYThG\nKUlubQe+nwfbiufR29G52Lxwnivnzmm/VjfZtKtrus4D86jOFUOCVN/f7aasbGubYxeSmYyphxEF\no6r/06/8Ry6dV8/jQ8tFBhfPA+C1tgktW/PChec5G1jtttkl5k6ql+rYX/07N+2f5/sk5uW5eu4C\n519Rr1eW9Li6ap7eLMu9KmmS5Ffz+CIE5jEW6eeekV6nMwoWDwoMbL8mSUqnrbJ1ZX2XIcVdCRwv\nvqZjFvf7PP0t3wroVTRDz63ve7lX8Pog5lvBC0r0UM9h1wtwVousPlvNC5YOertMlVXGFMMMS26j\nOfAoRLqf0ow8s293a4PUMg1L4oiHNf/8gKCo51OpWuXqlo5hr99ncVG9tcVSmaRhNegyqNr3nWRs\nNnRPFGohyf5rBONwpNmQYsuIwqG88RlYm8WTfI7m5+cYXpG32xzkgfjddoemhQ/8xuc/ycyCrtlH\njj/Eb376kwBc2mmQWsjD8lSVzLNkJueYsqK//W6ffjo6I8cumclp8CTN2CcbDWgR3Pc99QigGaMr\n5l0qTR+gale3TfdTfFuT55MuZd/O9afuJyoOa5w18/FvdRyFkk724vI0B4+prF1aXmJpRj2kn/7C\ni7zwNaWet9Y26Gc6VuJKPPTouwB4+l0P43vD2l6DPOPck9EVcPvBnVWmXEZm6eEDGSs5UDlF6Yi6\n5WRnjp3N3wQg6q0TWryElyTELaUBPFmnKHrIDsKHODSnQvvE8oOcXdfvvLge0A/sUMjIXZLeWPVj\nd43S5Ea5npL/z21tfIDUS+hYqme/n3C2q0rf7OwsTz2lk3fixAmWF3XzB55PZpWxjyzUiYxyqBQC\nfBufJE5IWofsdRerekBt+gCvXLbLWNOQzNKG3/vu91Kv6gLtJY5ePNzZKcMDutfLGCTqEt7YeZ1O\na38XAfsuG8UTOXCWOl2UGL84LHwmeXamBCEfelBLUZzoncHfUiG2/N4Ps/TdHwbg8htnee2spv0W\nCBn0lCZtiyMMhvf0Zbk73qWjquQDSXI6QQoQNyzzLu7iF42uk4yks7/SD6AH7HDe0zTFs9TgKAxz\nekNEqFjGTtxqMTCOfnp2gYLFn5w4ch8nTmrfP/uJX2d3Xfv4ge/+Dq5aXN2r6+vMzurav7DyClMz\nQzCASOMAACAASURBVAosG61OL8MbXvLtstFa9vw8bdnzvdtTphxUqyqgXLdPpaS/bbbbVKf0AApK\nFY6dUAHVb20TG78bTtW4fFXn6OXX3+CrX1Nlqt/dYt4yoC7uDvjs86p8RV5EamuwHAkz8xX7W/3c\nxf/wQ8d44yWlqV49c4UDh/T5h48/zfauKQbibitmqlgo5tl8TiRft3GvT9sqQjcunmf1krbzyvYe\nq21dM9udPqkdHFGxyLAORpo5nl4a3tuZ4oweC7OYQyZN71ucYj3U37641eDiC68AcGwv4pDF82Tl\nKRIztJIkJTaDp3/ldc6e0+9zC2VqXDFZvbxCxy6J9QoRFopGoTAqQJxloxiQYhDmhUxbrSYLC7oG\n0ySlZbJgeibIzc5+HOeFfdudDsNT5sVXznP2qsaCPvL4MWozprykWV5Rm7ECj97Y3toPsuI0u327\nQzDJchk37Tkqdv9doVTGK6qsbwwc2IXGXb9Pw+7P9DahaAVx280d2k2lnavVMokpx6QZnt18UOx0\n84zS7Z0mbdO4es4xGN68zYCeGVd7SY+GGZC1bgd3G0drP45J3Gh8wvyy4piyFVyu1+tsbaqitLW1\nzvyMnhNer03Zilk/+PBxTl/xbEw8MmtPp9MgRsdqrbFG2yhuPKiovUnXZcxGugbuO36UT3/xizr+\nzuU3hGus7FDzdNednzdHGIa860ml6mZmKvzWZ1XBaeORmvEsXpyXgqgUKhxY0Ji1Bx89lZfxOH7y\nUJ4t7XsZWTpcS44wGJ3r7aau4RNHF3n1ZctSdB4Fo0p7nQ47tl92d3fwzYDEGyfr5LbuIJzQfBNM\nMMEEE0wwwQRvA3fYMwUeaik4SQjMLZ5Ij86wCMXcd+FFWi+ic+VTyNanAfDcBnE6pMAcJau7NC1r\nPH1Mrf84m+XZs2rFDjo1ykMNnBYwDGIdBZSP387tHKNIu9u47uDru5jQtzsHa6UaTz+jNT2eefoZ\njh0/DkCpVCSyNoSeh2dUhOd5DB0QXtwhNisyC6YIF9Sd2e+38xvet3ZjInNjn3z4GYqmdXfihM0r\n5rHq9/PaPHEc5wUf4zgjc3blTKdPGu8zQFsSBMtUERCjmQJCnF3FkPmje8JEYPYpvV6n3imT1NUi\nLP+RP0h7Sb1zBzsNnvuiWRhBn3asVoU3XSawJ6VZltdmTYOUXqRt32l3KIVWcyXOiC31zg9TsHpJ\nUa1O2t7cX/9AaZFhtpcTymYJlYpF4mEAPRo0DZrFNLB7v65utnniXboew3Itr1XUSVKadvVOp59S\nNW9qeXqJq3bP2fLiIfxQvV0pGXmHHbmXJEnTEc3nGGOkby8AfdDusnxAx+3UwhRFyyJ97pU3CMpq\n9S4emMuzmxrrZ+hbNuL53TX+f/berNeyJEsT+sxsj2e+s18fr48xR2REZFZmdGZ1VjVFVRYllaCF\nREsIiReQoMWv4AGJHwAPiAcEpQY1Qq2CLjU0nTV0VZKRQ2TMHhE+D9fvfOZz9mhmPKy17VyPqkw/\nLkdBP+wlZer4iX33sdnWWt9a37rLcNUHn93C4z71qxkG2GI3/WrXR8FWY6gC9Lr0W9cvrGN1g37r\nx3/1Gb76kt5z+eqruHCdvK+f/fwrDMdM0CvIuwnQvrHPYf75jYYL0E6nUzy4S17izz75AqMn5I3q\nyRL7Kb30INfOEi2KEl5Q1T+TiBhmmE5meH+Xzpj3Lvt4rxdz2xQm3M7Mb2KV189bvS5GQ/JUjwqN\nKzy208yg4N8KBOCF1IYoiBAVy3lujDHwVQWZdlElc570j1DZyYdHA2fJN+MmfPYoUT0yGuNut4MV\n5o366v59TCZcHiYK0eZzeb3Xwj5DtdPpGDMOFzjyfZzboWy+d957E5L3R1lkLmPSQsGKBWz+PHI4\n9zGztF60Na52oslTzNnrFDdWkRS0bzIToExp/AZ5ijSjs9i3GbocYjKfz93Y69xizuWudFki47FP\n5BgFw+8PRmMc7TGRtBDY5uzMHQlHepmUFrqqS1lYnKTLL9TSaFeyxfM8BzMJKRy85fk+oiZ7dIeP\nsbdP3uDD3Sf46c8oUaLdWkP3InmX2t2eQyfWuyv43e/+Nr1Hvo+1TXrm/tFnOBkSkmMDiYePCK5d\nb28g9CvvWL5AbE6DN1JWVIJLyXg4cJ6sjd4qXmd4/4OvRhCWxjNutBwnlCel89bef3SALa7d+uDx\nHvp9JoM9s+butvF44sopGWPR4hCZC2dW8Z23Kent57/8FIMnNG6Dk100FGeqTs9hpUP7UsFflPPR\n5rm4tL5RZUpCQJhKwVGw1aWAEtU0paIB1X2NGhf2UAZ8Ud7/ZygmdBga5cMKcvtF8RO0GGO++XiA\nOwwj9fRjlIz9Z9G24/rWIjhFbvh8EN4yIpICBWO6f/CjP8Z3v0ckmMPhAB/+9K8AAK1WC2B3cjKf\n49VXCUteWVlxcIhvcpT8WcY9jJh1V6PAiF3Ud+8d4MEBbYbJLHHMwsaYU1kgZpF2WxTw2IXcbLVw\nfEKEqJP+dPmCjtZ8LaSM6zlJBamqLDPr3mdgUJ4hluC1H13HmONrimtnMeY03tXts3jrzZcBAD/7\nm88gtklxvPjKJRx9QfQJQRRRlgkAiwJlylBqmWGaUT887WE1pgMkFxIZQzNC+a5o7TJirHXEec2o\ngYDHzJQlfLXYMtJjZaHpQTMr/9rWeTQ4w2cwmaE5oXa2VlbgcyZVs9XDgOv3+Y0Ojplcr9lsIYiq\n2okLl7qFWLifrVoo/UK62lrCaFfwexmRAsj4Qlxf62I64dgJhCgyev/k+ATpCdfxQolGm+bl5q17\n+PIewXB7x0NEbTqcPShoPoh+69tvIGGy00hJ3LhIeEJDAYWktb+7O8Sd+wzpThNc4xTmfCKQMKmf\nFcpRfkAazNPnKFg9m+P4Pq2fo1tf4tZDuhA/PZxhzmdDK/KQVkm8Fg7G9ZRydSGLLHdF1nurPcw4\nC/LmcIzLrMzo8RijvMo8beLCDkEUL6+t4uF9WvPpw5vYP6QxLwsDyQpG7gGeXzGsK/jekvMoqCA6\nAFy4ehm/+illS81nGRRnb0XKR4WEHPcHOL9G56nfDl2mUhRGeMLKwnAyRpbRH5z0h4g4O3P/+AgH\nQ5q3TMNlvXW6TRS87j767LZbplEY4iwXxj5/fgNNhr6B51Oo7j1JgZDGNfAbC2busAEZ0XrRsOAp\nQSkiIg0GcJyeYDSnfbYSR5ikzIQ9nyPi9ifWxwy0Lydl6Ypqy+IErJPhcZJAcW3UuN3G7iEZZj+7\n+wglG1RFbt1eNPCQMo3IMpIXhVOgLl24iIxDW/aPDnDCtf88z4fWVTbloSOSbp07D2+NFLr+/gS/\n/fuU5f7mS69hpUXGaugrCEvt32h4CFK6RydHJ4gaNEeDk13c+oRg9pVGFzuvUKxkf7K7qGNJnQPA\nRcGfg8w6SRIXv+gpi5gh1/n0CNMjan+7twHFYS6e58Hr0ppZ772Kl6/QHXLxzOopku5F/GgQePBZ\nD4ii0CUjRo0WGlxk/S/+5T/HyTFD4cqis0ZjUpbGUUrAE+6OtNY4JXcZqWG+WmqppZZaaqmllheQ\nbxjmsxCurp5cVKm20mmbRmTO0ikamwgv/wgA4PttTBSVPNHTPhTDKloDH9z8BACwe3SIEUNj2/EM\nkr1gqX0Fc5+p/r0d5CBt3IrCkRXKU54ALQRU5VqxFgbLB/bm8xRnNyjz4+3X38BqhyyUf/o//4/4\n0z/93wEAW5sbsGxhBb6Hf/yf/2MAwObGd9Bkjd03EntPyPt2dDCEDkiLjpo+/sk/+RMAwN07u7jy\n6rsAAOWHzuLTRrsq9MZY57a0ukTcYBeymUAxzOdDLA1tWuk5C8yKAoLLTXhCQVQWjDAoGE4wsK76\nur58EQGTBu4Pc0cm2GvF2Noki1k0mlh/meoHxtseFPOpbKx1cDLi4MEMKG5zcKWdIWHoWHmBg0Cj\n2KJg93070iiWL5VF8Albrp7nORK3RiNGwJZ6qTVChmZQGjSYv2vn6jUwDQ2e7B1gPmdCLN9H2CZL\n6969e5gyp4vwAlx6mTyT49EYpqzqT562iCxQlYw4FXRsrV1Y+eL5yjtAWvRH1IaszFAmHNQuIgyP\nyJs2tVM8+PRj186rN2j879x9jAknf167tgMVcKDuwRTNHlnD3/3tH+LHf/6XPKAFegyZXdrexCAn\ny357owmjCdq7sfMaruyQR08WAe59xdmd2iDhmmqtjudc+cvIrf/jf8P8mIj5lC2wxbDNfivAI/bE\njbPSBb16wjoYvLJyAfJMVZBuFPlYX6M+rnZieGfY41ZO0OJMqtWej2+9Qv3aWGljqyCvzxeHChnX\nIFWwqI4VIYCAM2s9ZV3JpWeJ0dp5o5P5zNXgO12zr91qIGaoSHrS5Sj0VlccOejd+/dwxN6Wze4K\nLDtVhE5dTdOP7x/AcmZ1M24g4Lp186TA448pi/CTD3NUyZPNdg/nLhJR8g9/+C3cuExtCDwF8xw2\nvE4tJn2aw2ar57yyxgMKTk5JZnMEjYrEGa5fXhhhwPxma+0Wdh9T0ofWBdZ4Dh8M+hhwwP1oOkHG\npKqhFyBkL3F7bRVNDrMQQmI+IPTj0/uPsMHnWZFkEJz12/ADasiSkqep84QL30fMXHNxMoXP5U8u\nXLyAj35GIS+j/j52XiLoKm5F+MG3CcnZbp3D7/wOEaX6RYlS0r45Gmg0ONHj+stXces29XcwnyIE\nc29lGaZT6tfOuYtYX6G9+PGtO1BM+EkUd4uzB8/htbG2dF4emn/m6jLaoTGHe7fdGWa0xtYFugc2\nzp/BGpdw6iYpAq6/aqWELSvvfQHDmcSZ0RgwgW05nOP+A8quz5I5oqDi7isRR5W336LgMBdrTvGg\nCeHOhmXkm63Nd+r/ITTkKbzIXR5WVvcGrMlQCFqs8uIfoLlF2QCd5ACDmz8BABhf4P/5hEjLzMkn\nWOcaTbYVYaVZHYgHGPk0GUHwKlKPYlrm3gYMu3iNsDCSFRDAEYFClrByeXemVQLrW5vcFYn+kBbo\n2fMX8Qd/+EcAKlycOtntdtDkhXswGKDBE9z0JAwrKvvHx0hKLkg5PcLHv/wbAMBoMIPPmSurm9vI\nGWbQWrsUVmuNWxy+72N4QofjB7/41zh/8SKPScspd88SvS5h5zxOGeBXSvApCjchFtmTMBoHzH69\nf+kdRCsECT3an8Gyz3jv+ADDT8nFfObCNeiQvp+e3EWzwXXrjh5hPGY3sRcg4MqsxhPocnrsSd9g\nnNHh2VztodFiAr6swKR4vlpZ3ik4r8oA8TzPuYC1MYvaamaRGTpLC5y9SBepFXBQbdzuuPGZj8cO\n4m52O7jEpK13vvwKMybAPA2FfF2BeupzdQhb8xTR3rPEVwG6HVo7UllINmDmJxPAo7Hqjw3+6qeU\n1p8a4Icghffg8Ai//X2Cr1999QqOxzTmf/6vPnDjlmQGT54Q/LrdjhFxH4XQSDmm0JYJrl0mI+fd\n195Er0fjPDhIcLOgv5VxCaX50sQMyXMUyUX/CXpcz1F5MQyPz7teARRMSzA2i1pcnoRXZf8ZXfF0\nQpy6RCaTGTotulx+8PY1/PANiu/0ihRPHtFvhYGPBteEe/TVh/jgI1rbyTxHt4rDkhJBwDBxKF22\nZlkIt3efJbrUsBwu8PDTz1Gm9FkpCcmxbkmawpR0Lly9fh5bW7T/ZqnF/i4Za2laotujM6sTh2g0\nqI2+ytFeo7PJX1vHl7cI8j3TayHmC+3+YR9zprQwOodQVRUJhd19UoI+/PRLrDFMtrXagXgOOPo/\n+NHvI0moX/O0RMVRCyngK1ov+Tx1sXGlLQGmgghUgIIpSw7v3Hc14B4d7GPOWl9jrYWQz9/LUeTg\nv7gZo81xZPMkR8awsLUGK1zT8qNx5mCmrfUVTBk2D00Ob3kmFgi7OCcGoyHaDG+FYYiSlbLJdIxZ\nwnUV2wJgxvcyA7Z4bK9ePItiSm04uPMZRJu+b63FeHxEin5qLIbTihx3hDbofp2fzDFhJfH8pfO4\neJ4U4fmPp25swzBcKDunal0uI0otzjBjjctCBgzSlNpjtHUZ20JYDNgQunvvkYvpM3mCDtdNjUYD\npAM6S8rcuHUemhLJFhk5yg8xHXNc23wK2GpTKwxHNFb90RSbXGPTnso8FUI8RTb7LKlhvlpqqaWW\nWmqppZYXkG/UMyWE+PVokkMr5ILLRyoYU5WBKdHosKuv28BsjzIPjk72MR6TxXRBnmAtJF4c6beQ\nMX+TnR6jEZIm37YDpMVnAIBj+RKygIIY58FZZCD3qjJUMw8ArCwgxfIYkfQ83L5Pbfsf/uR/cpl6\nGxsb2Dp7wY2D4eC9whj8ix9TYLrnKQRs2YVSYDrmQN2DKUK2sH2ZYp2DSJUFBifMz9TwXU0nKaTj\nhLFYeC9KK7H7mNqWZmM82aXg3K2ti7+ByPRpSbshfJ+ttGMNxdaJllS1nNploVzAdIGTI4IBbh/v\nY6tNFsM8y9Dn4MpcjnAyJCtkKobotrhMS2OKvQOyHoa5wJAjQnu+ciSjRmikXHKmsAI5B09HpcDa\nFlkh3qBAFi5fK8vzvUVGR1kgjDnDTmtntcRRBBFUlnGCPOdMvTSH5EDzdrvlYEGppAv4bXXbjoBP\nW4GUoYjpdEGe+PX6ZY5j65Rniso7nIKpn8PtnpelKyfS7jRQslez3YsQgCzj/X4fjw+Y/0gbGI9K\nPm2d6eC971KW6qWLmzAhQQ4/+cmneHCP9uI//7N/4bw8YeTjmDldfNFEOucyT6qJa1dov3YaLQet\ne54Pn4xPpOIEzTM0FsPpEbRc3jPV6ISLenwlEHJdrtWGwYTjoffnCrqCjnThAs2lsC7I1/clYvZY\nXN9u4re/Q/DDD9+5gtWI1mHQCLG5/g4AYDw+wZAhv8cPdpFWeJ6V8Lj9jbaPIKy8YLZCPaC1wXi+\nnJfYAq5szMNHjzAak4UftzvIK7gYCop//97DY4ymTPzY62CLs2mj0MOQyUfjyMPGJnkBmp3A1fJ8\nO+5ASiaozOfImPRyPDpEkS7ql1UkmZ6nkHNQ7+2v9mDLXwAA/u1/8C7Obm4s1T8AsN4+VJPWddxY\nBHlL6UEq+t3eRghG/KCtRMoeCuUZxGdponfvTx3BaVqW2L5OCQLv/eBNVPhF04/B9HVIdYG8OhPN\nIpu2yDUsw8WTdIDPPqAzdOeNlyEGNOdFOoN9jlqgUiwwmtlojGRKczGfTmA5G1joGaYlnROiG0Fy\nckTXRkh53xz0j9Bt0/iP8wyH9yjxQR9nmE3p3D+ZKYgG3R+bW1dwj8l3P/75J2hz4swbb72BQNIZ\nlqUJEp7ftY1Nx9X2dZbGZ4nvxw6uN1ajzQkJrUYTxtC6TZOJq1cJBcz4TOqfnMBjJCeH77L8zCdf\n4BEDSMoPEXAsRygMLBM55wY47tP7S0h4THBKhJz0TiWFu//sU55v9Xz1B5d+8v8DEeI0L6b42n87\n9W9XL09C8WaGKRHOCQJ5cu9DIlAE0N66AbFCsMpkV2OmOTPK+EjLShHzEPMmsfMxhKKDvSUO0bBX\nAQChvoZMkmuzlGeQKlqUObpQ5jkw/rzAkC+OR9JHk7H8sjQwlrPzJACOHVtd24AnCW7xlY+AixsL\noWDYldvsxS774fGjuygDWvTt7S7GY3JzelCuAC4tiEU2n+BRL7IUBRO5Xdl5FSNWZtrNEKOTJfuX\nCUhJC9KI6SK2TNhTRaMBzy1CD/MJZWzt730JT9Im6g8PcZ+LwbbPt3HIrv9P73yEnYJTjzfaGPSZ\nymGti/GM+zEvITSNjS/hivF2uj34TVaIA41mwamv+Qhx68pyHQTpJA2GpWIvgOeoKwR8PsSiOHYK\nSDbXEHLhwla8NsNGAw1OhY/iyLnI57Mp0qTKTFSI2DUPIRaxE0+5lxc5yaeVKZLqEMBzJaeqSLg4\nIPghLp9jo2IMPLlPh/n+yQQlxyQEsY8RM2C/eeYKzp/nC1Fm+OIrIplM8wEmXMz7+ERhc5UuX6kK\njHN6Z7cMAbHCY3WIa9doXqJWgMKOuG2Ax8WWjTcHGjTOwpxA+svvxVAKxLyulG9c8W0IDyttLqY+\nSpFzvTdfRggrUlBrMGV4KZAKZ9q0R3/vWxfwh79LVB9h1ETJ6fmTyQmygvbTk/4Md+8+ojE5mWCN\n9fhwRSDk9gsf7pwTsE75STPrCGCfJdYYhAzzb1y+hCeP9rh7PmTFGl9qFF4VSxni8JiVP0icO8e0\nJrJEO6oUWQ0wGzd0hMEw4XcK9DjLcFYo3DmkvibawGcW8LIskVdEkdMRmhVVRJrjPheB/vLLVVf4\neRnR4RQFZ+VmuiTFE3wZooL2PEfLUhaLsAblKWjODvM7BgnDy9IAYAPZyiMUfH4UaMJ6C/hJVcZh\n4Lm964WADOj9V26s4f33yTC//XgfHYZ2g0BiVCxfIDcvC2c8zSeTRVZ2WaLCC4tMIvBIyd09OsEm\nr6mW0njymOa9H/Rx8SzdYWJjA4+4Ruwnd/exydQCl9ZbSBL6rTs3n+Dep6RktZodfOs7lAm4c3EH\nP/sFhc7EzSb6XMz5ZDBEq1lBkAGep1KIMQtFWBnlwgG6vSYaVbau8iGTqsiwdrD8F59+gSvMbu6J\nEkVG62GSZ5ifsCE6m2HM9UKPkwKzvyQHxaDfx+5jGgffC6pCBjA6h6rImD04ChgphIuWkEJAPAcR\ncg3z1VJLLbXUUksttbyAfLPZfFh4oE4H1QohvhZwy9CRcAkvUDrBV3/9ZwCA/U/+Gte+9V0AQLnx\nBtau8ufmCmYlZfYlxWOogtyu7YaPgIMVhfZQcPCpLyaIfSI865oTlJI0/6G8hKlHbuBM7UDLc8t3\nUJfI2Otw59ZXCIMq8Nmgf3LgPisOYjyzfQ6CrZLJeAJfMN/IzhXcYK6PRiNAwZBMmhforWzwOBmk\nTD4nA+kgN6ONc8ISxMe8QbNjR0rY7axgypT7x8dHp0ok/GYRx3Norr4uIWAr/iYhXekOgQXkJzwB\nMBQynH0ETYkVONq9h919GvvZYY5JTrBBKTRSLlHTRwJbVWgf9CFGZLUcDOco5uQBObMRAexF9MMA\nnQZn8pgC8ZCDMYcWq5cuLNU/AAj8EIZd+WEUOpgUQkBW/DRZCsuZPyoUmM6oPQcHj7EzJW/n2tYG\nBHupICUqp0cYR87zVJQakjmG/FCB7TLo024mS+V0gFOZJuA9dJpRzyxvKQbdEDlnvLQ325hyuZ1s\nAkzYY6mthWBPytaZNtbWyIKcTkeI2CPy+OARHh8Q1NFuWZzf5qDdMsHmJu2hi1tdnDtHEP2ZrXUc\nPmFYzbdYZxgmV0NEa5yBNmxi75j2ysHRHkRUQaIJPLbOl5FNv3AwaGbgCFSDVgMrMY/j7hRlRvvv\npZ11fP9Ngiw7jSZ++QkF39/dO0LK8MOT4zn6B2Tprm1fhOUx39t9jMGUg9TDdRyyFzWeFWhyrbg4\nllVVGsAYCMamJikwSapEguUziIQUMJy51mq2YRnCWOm20dyiHz08GWHA82myHBEHlIs4hIlo3zzZ\nv+vqDYZRiLQqkTOYwDLe6gc+uuwdPzo+xpi59JqNDppMyAprnMfVQLgzALpEyfB7NsvxPB6NlbDn\nkjWMtahYjU9nLAsp4fN5GsjwFE+QXSSMXDC49z6XKOp1cYnrhfaiBjQjBkVhYPgs9uPQJdd4nufg\n96IokTGp8MVz62hzDbgHRwe4zvX7en7TEVIvI8ZaWM52KMvScU4FQYioSfss8GNs9YiL77OPfoz3\nb34AALjdbKDP8zWfJ5izt1lLi8Mx9bcwPh4+prEa3n+Mk126Fx8+PMTVq+QZvvzGFUy5LuyP//yv\ncfMenc1Ro4UOb5V5VmAyq6A6hfA5oMw0yxFwWIQSEobXfiNQsEysavICkj2DYdBBdWB++MFP8fkn\n1F8JOO99nmdINfU9LwvYtDobSwdPC8DB9UoKWPZCBp7ANsPc3W7jVPKUcF5CKj33byjMB/xteO/v\nkootVwoBv9ows2Pc/ZQGdPfWHcwkHQRn376M9iW++LbeAgSlmZf5Leghkdhl2RFSLp4cCsBTrOAE\ngMkI31KYIGQi0KZ6jJAP7XuTczAbv8ctezbW70mBVa7zE27GCHzaDPP5DBkrAHQ2UB+3ttZwgYu6\nFkWBBmeObWycQcoZJCeDMaZ88Y0GB7jAF5PnKxxxzJQfSRczY6x05KhGL7KVyiJHykrTxsYGBn2C\nTfcO9iGXpH9QqYURDLsoCeFVc7XIIJIWqCpxCs+ge476FHUfYPceXZJ3bx9jOiH38UwKlJy+DU8g\n54tFR6biNkWJEm1F/8gjA66LjP2DhPy0AJotIG5VpKcWaUrtWetso9WKluofQIenlIvxqA63UmtH\nk6CUQsyxVKnJUXBDx8MB+kfUx40zZ1xaui4FqrxxJT0I4WhkMZ8xFcF04eIX5mkDo5LTEJ8xpwhU\nDfAcDB44d6ntjIq44cGb0x5aPbcODi3CbFKgZN98HEnEMbW53VIoCzpUz5xbRS4pK/TWp4/x0vep\n/uQHN2/j7e9Q9u2NSx10uhyH40t89PEvAQDbOxHOXKFGT/MBUo7Fg8ow5OLDjx49wMoZyvgrrXSx\nY8tIKXwYr9JgQ8ScpeaFAuqQfmun08aQoamXegpvMj3DZDjFNdbb0qyB2wc0vx/ePsTOWYJGvt9d\nxeEh7en/66d30GeFIW4fY8Ojd653PAi+rKElPJ73zBiMRjTQSWZQ6cieMDBLxi9aazFP2XD78hbm\nXDA98ASygvdT3MBlhqzHyRgHrKTqNMWsCn1QIZ4c0vdKCHQZhtNyjrhJF04zti6es5CAxzEvKojc\nuSOVj7jN9R7NApoW0uLqDp2dr71yEWGwfPwiMgtdcIyMMfBCnkMpYHQVDmKgLWcyRsGpjEy42p6r\nKx20WRnsNrtY53qeRZpCV9APJDRru4U2iJgewxdwkGLkB6hYMy6eCbHJBoaAxbXrBEXt3d/H/hCt\nhQAAIABJREFUml1e6XdxQqC4zOqMuXTxAlbXafwH/Qk2Ny9wXzbwi88JWvehUAWMmbTA6JjuvFTn\nLq5KBREyVpTuTyeOwLq7voazF2jv3vzyS+wdkUF18+4uuiu0ZpSQrgapETkyhlPTXMM8x14syhI+\nx482mg14Ho3b1cuX8PlX9wEAJ6MBZMAFv6VypLZSLArMSymdAbnS66LLtA2RUPACev5oPMI9jlsu\nderIRav4MwBodrq4fuMGACBuNF1B6cD3TtWRBJZns65hvlpqqaWWWmqppZYXkm82AB2AlY7x8dc8\nZVGZ20Yb5GxtJU/uobNOlPLjIoBoEV9KlqTQI/I6FY0Y/TYF4KnWeTSaxCc1nN7EbPTX9PrRF4jZ\nOlyRHVgmfMxUijl7fxpCQoAsteHREVpr31+6j+1GjCZ7I7Y2txYcKdMRGgyNCGGd1i1Mjohhnlbc\ngGAtejjYw2PmaekPxpjOOJh6MsPJIX3v+T4GR/T5cO8htF5o0ZKtttl0hn6fs+ayFJvbHKAohAOI\ntNbOHf4skfKU61MsIFmBhfUmhCsqD/gCGfNlpdMUj9kyVl6AZs4BqoGGiMnTWOQJDg5pPiOtMQvI\nSu7PpzjDpJeeHaG7RmNcZAqJou9P5gY5u6GD1GA8JktrfctARfeX6h9AAfyu7E6ztajvdgoK7Xa7\n0OyNmk+AFhP8JdMpdu/Tb62uraHFVp2IG268jbaOZ0UXBQ6fkDs+m01ROVKMMU95pqosmtMB6FJK\nl8AnbEU8u5y8+3oPShE843kKKyGNc9OL0OHg7Ntf7qNIaBzG4wQ7f5+s2N//vW8j8plTp1Hi7DpZ\nk42whUvXaI/eerzryh5dfOU8pXsCkDOFcZ+8qZfebCMLKeNoZnOw4xHNsI3XX94BAAQtg6Emz12S\nZoBdPrBXtEKELQ6y9iUWzkaJsyvUnuvrGk84yWFLD3Hw6fsAqCRLjxMYLnsKXNYNw9Lgx5/QGm53\n72E2Irjr3u4IGUNu28kYEXtoMlWi1eMM3UBhxuM5mZWw7CMIFVBWLHzGOuju2R0UULzn8rLAcEbr\nPS0y5xVqdjrQ/Ey7GbnSIEdHB9CcXCD9AA/5TJHKw2oFaZUW13fIMwIVQARkvbcaIVpM/jvXFqbK\nrvIMJAfzt9otnNlgoshXdvDu268AAFZ77efKOjVWw1RloYyBZW9qEEeudmJZlkh4b06yKSQ/I6SC\nYKdgalJsnyWPftafYdYnz6fYtlDstRalgLRVGSO5uKNKA5tVZYwUwPU/u2GMN69RCMhue4DzW7RI\n7nw+gBW9pfuotXUcge1WE1FE7dlcW8fOefJ2/WrwCaKQFvAf/egPEDHX0mg0QsFB2+0ogmJP8sNH\nDxGyJ2ieztDi+ozR2TPY2NoGAMStDu7eJYj+5q07WN2g/W29AAX3PfY8ADT+jdBDVZAvy0uXrbmM\ntNsdl2FujHYe5pX1Hl59naD1qBlhf5/mZTTcezr4u/LYC4GMz921l97FGy8Tqfe1K+u4u0f79eSX\nf4Ysp30ppXJnJ60jF12Om1+R96rZbGBrnUvLGItKRTH2VMbcEvKNKlOlLDFhhSUMO/D55pBGw6sy\n+ATVAaPvS5RcT2mYC1x4998CAGwYhYALcOpUw0zJ1b5/8AC9G9SloLEG6XOs0+oqyuYOfQ5+Cj36\nFAAgJidohpxh5wMxu58bIsT9jN7fuvI78OKrS/dxY7XnCh1/8fmnuH2LJixJZgArcb2VDm5cJxej\nsCUePaRnpIDDd4218MIqBqKJzz6htHRYheNVcpnHURPvvUPp2FGkMJlUrNEl/Ary0xqHfFC+/7Of\nYzygC+7O7TsOItrePgtTLHeAKyUdC7GFcrCtwCI13wIoZJVKXKDYo4WtI4lmTJd2NPVwktGl2mpL\nzBT1NS8jzFPC9JNJjgEfdGMjYDkOpZEDVV3suCEx5+/L+QwzcAHWSYFsTpvu+M5jZPo5WPROQdFh\nEDhKg9Mu+DBcpN1DKLfpyjTFyQH1a/fBXbTbpGisbZ2DxwqXEIE7qKfjCUbH1F/o0hX1BZ6G9P6u\nz9baRcyU/Tpr+m+WnYur7nCLGxGaVZxJkaO7znELDQGraKBVmOL6K3TYXntlG16lDBoDoUlxKDNg\nnRWrly5t4ugxHdS6OO/qEpayiXFOa7D0DPZHX9I4FAK2pMM/lmdx7du0d4d2F3OuBCCF52KUlpHW\nRhOyYgDW2mUmllkBxUbOmsjwK2bovxdYrEZcJwxwNfs2bIF2i95zL1W4uUdKy48/fIR3zhMsuBZo\nHI9p7a0aA8VFoVc3Q4QR9etkVLpi0Z7yEDFEbqx11SC0BfJyyWoE1jqF4syFc/jo08+pf1q7GJwn\njx8h4liVyAsgvap4s48mw54H/QFOuHhsELfQ4QLY5y9uoNUiIwfKxwlDRY2ogescarA7mCNwhdBC\nXLpOZ+XOzhlKDQVw9vwZrPbonWQkLL9OrbDQFdGisbBMQWIVIKpwDU/AY4MtKzO3J2xeujhO5Udo\ndFjBTWaYMHFlOVaIuEB4U4Qu1iloBtCs7MZhBMHjlqQlcs6UhAS+8zKRtm61A7Qa9J71laaDlJcR\nKSUiVnaUUrhwngwSbS1+8oufAQAePH6Eg2M6V1ZaHVxgmp1Obw0JZ9mm8xkmI9orV19/zbH4f/XV\nLVfOs9Np4+59ClzV2rpMyWanB5/vPwvrqlN4sefoApQUUEztQSS/y89jGC7O0bwocchkm7cfHuKw\nT9+n6aJeaBR52Nokpa8oNVImpE3TFJohuUd7T2Cb1Jd5t427D+ksuX3nExcbRf1zqdBYZfLPMpnh\n8884HGN1BdubHNgoJXSVZWvFcxVzrmG+WmqppZZaaqmllheQb9QzlSgAOZH6CT+HlgTVSbPQci2o\njg9ADhDFrsrNy6/AcmZRUhpXk0fmGaZ7FIz35NGX2HyZMh58YalwHwCIAHm4AwAIt5uwHbIm5qM7\nyBOynjF5iJj5Q6ZeALP6AwBAY+3vIXuOUbqxc9kFLzebLZznDI979+46T8C33v4WXn2dMvXUqbIl\nUkrn2lRe4Hg5JtMZcq4zt/v4CQwHLK5sbmG9S2PoBwpdrgBurXHlQZSUuHjxGgBAGw8ffELW62w8\nwptvvUl/6wuXhfMsCXoKJq/myqKqtGOxyDQTEC6xzJYCmFWZSgrBlPqXHUwQr9N8RusxJhwwL1UD\nFcoxyxLILv2WH61DhvQ51yVsQhZMHGiIOfNlycJ5rFpRjGROlutsNkP5HCZGWZbOkms0Gg52KXXp\noDellHOjz4Tv+NDKPMOcyRPv3/7KIQWvKB8tzsKEKB0kNzgZYDKi5+Xf4pDiMcQiO/JvlZNxD/3d\nf/trRZTwOLC+1QoRseUtbQDpk4czarUw5yzSi1fX8do7tI5smMIwp45JNJKE3lOUBaKI9u733nsJ\nn39Ba+rB3l1Yfj6UGzj3Oq3TRKR4vEcQnt+IUXCg8TB/DHmmgnAAJen9BhK5S4d7tihfQFUen6KE\nnnECwyyDYl6n1QbQbNA83k8kNsb0/KbKUJwqJ9NkjqULUYF4ldq/NynQZ0/AW1sCDzjBoB0C29uc\nueQrnIw5IxUGEddzlBBufwtjHF+bAiDlcvNo4bg+ceO1V/Dhh5TJfPDwEQxn7fZ6PWSc4SV9H21O\nmkiyGfojTkDZO8RLN+jcvHrjCoIVCvhvNkLkI/IuFSUwZwJlmWdocibgWidAXKWp+j6un+eyXUix\nu8tkypcuPUU0+3clVvw60aVBVarQDwII/q1C+Ug40/dgeIgOhwDo0iJljqcQwGqHPBG+CpCmBCF1\nLrTQZPxvs7uKjAO4IxGgy6WURKAw4SSL0XzmsrJznwLwqQ0K33qTzvGL2yt4tEcZcP/+v/fHSPVg\n6T4CC6+y0QYPHtB7hpMxTnh9hb7vQic2VlaxxUkCB599jjt3CSovygJ+VSan1cYxJxhVpJUAzXXG\nhMdlWbhM8manB8VnQJ7n8LmGndb6qaz7kLOZhRXIsfxeTJLEwW17JzN8cY/atLvXx4NbxDfYHzyG\nLWnuPOnhwjatwzdfu+4IP9MkRTInT9w8mUFP/wIAcOunP8dkSv3Nxvsoq3vJaMRcP3H74kUoS2fM\n3uM95yk7OTlxAe6eHy64/iwQnqrR+Sz5RpWp+fwI3oguc12uwgvf4/8SOFciRUzxZyEgOGvEEy1U\n2aZRqVHyZarzHPcf0mQEoUbMJG2FAQQfNdIABU/kTG1BNSll249eQpCQa9Aef4C0oM2WhFsI20S3\nUNgtWDFduo8Xts+5SyoMA5xlfPrbb7/tirQGQQjBQ18VDwUoNbci7xNCuwnWWuM771AttN96V6DZ\nbLq/mXKRzrJYxC9ZK11GpMGCdf7td97D1ZdIgRJSuJpzeZ6jjJeDwfyVFD4Ti/pBjIQRKjPO3abT\nsA7+k/AgWBmxqUYxpGemswlWOI3eehKKMe40TREzHJb7OfyIFn8ADc0KSxgFkMyiXdoCUZM+d9qx\nY0KOdAjdYiK8cQARL78p4kYTYUhtWzl7HlU+uwxDjFnxiaIIDXbN972+O5Sk7yNjBWQyHuP4CdUl\nvCUVzl0mZaSzdt65j8fDkcPoA6lQpXUZIRwztwUgKyI/a9x8GmEWBLcWLt5uqT7GIRPvkSKeJFxn\n0EokTBqZ5AWET33/7t9/Dc1VmvcHR/dcvMRqaxs5KzsrWxFuPyQIvb3WRbjFhZGPnsB4HG8TjrB1\njdav523CY7Z1UeQomcokMyPIuIovjFAW1R6xjo1+GRHWokwWCpRlI8SXBsx8AWUsvrdFczdODEJU\nkK4FJ45RrUle210P6HTp+e04cobfSq+B1R5ThkiLjGPEpqkG3z/wPLnIcIOA1FWcFJwBIYxxBILP\nEimEK9S+vrWJV/lif3z/ocsQ08MhGtwA6QukfG7OZhPsHtNafuvll/Ctt4jRPm43Mc6oMYcnJ/AZ\nirRCQHNtO2sMsmrNKg8JQ2/paIif/N9UN1T5EgErjvHHn2G4T3N741tvwAuX34tWCPe8sRYHI7pU\nnwz7ODghhWW/f4BOi9aUzg1mvH6bzS7WO9TmlsrQZvJlmWeuILOeKBwzdDWdDNDkWMC5sdgf0viM\nx1PALio9hLwENxor+OMffo/6uKXQYmPgnTf/PobjB0v3sSgKVzEiSRNkFY1EmrmYuFbYwOUdcgJs\nn93Gg/v0/tFw6PaxkAJbq2RctxstHB/SmPueQMJxVUJ6WN8kJSUvEhi2esNGyykRSlsXp5ak2amo\nB7HIlPSsi4NcSqzAHkPJn92f4PCQPu/e/QKrGzR33/3BH2Hapza//5Of4GCP6nO+/upV9LiOqGkF\nKEt6Ps+7Los6L+YIffp+vxG4unueF7i6s/PxiYMR51mKJp8x22fWobyq7qvnlD5h7CnG92dLDfPV\nUksttdRSSy21vIB8o56p8OQjxCBrwiszTAakXcv2NUeRc5qHysJAM2RmseBREp6PBmuqSZGgw+Vk\nts+dhxLkUShQwojK2g6gXBCjhOWA2dTrwXYoyySIt2Fy0lpl0EGiyHulDSDs8rwokySBx/Wy0qKA\nx14cpTyU7HXKygQGbDlqvXArComFfisWGrIQLrus3W47z1RRFM79mee5c5+XZekgRc/z3fdSSrRb\n7A63xgVTF0XxtZpEv16MnsNny6zT6yLj4F09JW8KgL/NzcHz1hQBSq6jlzQ8eD5nNELAjslFm85G\nuLDCGUErAhFnBA0mAfoZWZntpoBgkr7ZvHR8M1mpUcqqpI6HLgfwt7sNpHr5LLBOuw2fIeVSAzkH\nDfu+52pfeVK5MjO+7yHjbJxGowGcqqY+OCTrSugCbS6b0OqsYT7jQPm9x1AlrYVe5MFWkCIEUsaB\ncmthTEVcuODmFFh4JiyEK7+wjPh+6GDZ2SyBLtiraErklsZ5mvcRt5jsNpzj5m0qnZEWJ45/qj+z\nKJijavtGCybgciIYI2ZCw2kydsXaR+UMMXtBe0EXVV6AgIGwlcdiDo/JGUXSQFFW4y8gn4MMsRjN\nINgq9ayBZY+PwaJiSlEA64IasRpZ5zFU0roEg0ILF+i91pAQkqBJ3erCq8qVlBbSUFBtf2QwZkgx\njpUL1lfWEvEkiHC3Cjq3ckFmLIXEkjRTkFLBq+rETcb42S+Jh288zxBzLcrJdIqUPeL96QSavaYC\ncMHlRZ7jb37yE2pvp4tLlyjRQBbAaDjmdnkQ7IEqdYmC90RWFCgrj3SeY1aRC2dzBBEHc/uey161\nsF8rlfSbxY8CB1MWeeEISKUNcPkcZSZfu3Ie1QKbjBPsHdP8fHlvDx8PObknFPj9HgVtl8MJzm2S\nB+f2x3v4K65Xmmvrjq5Sm4oq7ykix9xqaF4va1EPv/NDWuOf3HwfyZDOsHe+24DKlr8ziqKEqRIl\nYBwiYa11ayfwPFfyaXd/D7/4kMq9KLWow9qKGw66GvT7DiloNFoQquK70wjCqiRWhOmM5tEa4zKJ\no9h37ym1dgTQUnqOeFiBiEGXldKUuPuAkJ/HD/bx8BadJTs3ruLf5TI25298G0MO5fjq888dL5Wx\npbsVg9BHFHFYQaFgLXusigZWOzRuka/w+ZcUmN4/GcIwufJoUMLnO+fS+U28/holgb3+ylW0GLnw\nwmCRWKQXSVXLyDeqTK3HDXgVHbAKMWW6578V61HhxwLuwBHWODjEAg5HD5odvP5tyvJTqoWMLx2h\ntMvqKOUCJvFMAsk807ktkIOhLv8SJC84CwsruCaVnEOZeOk+ztIcklkPPa+EFFX8l3RxOEJIygDj\nrlZKk1LKufitXdSY8n3fQXLtdhttvtDzPMd8voDHFsNnneJmzNOFcQtXYHfx/rIsYexy+HeWelA5\nX3RpBo+z8AqvcIrAApyifwiGgVqFwogzfLrbMVCNTQ7EVZyDV0AweaMKAwj+ra1eCxkzmksFhBzn\nYooc6Yg2S+lbZBybk8BDUbnIhYJ9jlgbaQokJ3QgD0YJequk0GlbIuFs1CCMELFSG0chCg6wkYII\nWgHAmhytdcr2yrMMacL0FuMhjg6YPDEZocUEj6HQLvtIQ6LkBTPPSmQlK1NQ0KcgvyqeS1sJY8Ol\n+5imCwLXMPBc7cfcjHFuh/r1D370MjPXAV6Y4qhPiqEXWPC0IDNDVLWHty/HiANKpc/LBCEzcq82\nL8KwgTEuh/AaVUr1GFHMSmhLIiwptsHLsUgUmgIew0Vllj4XsoAyd4XGjREuE7AoBdKE0//zhQLl\nKwmvyjAWQLWS40BB8eETSkBJLkC+/wRxh+LgVCQx7lO/dK7hwMhUI+EYwyAQUIxDSx8LyMQu6pYB\nFsvqxKd1kjiO8PY7BNUlswSzMcXaWF9gzNleQilY1hBsaaAY2u33R8gZLvE9hSePqa7gxsY2krSC\n/yUKzviVvnI0KHmpXRxbls1h2DA4u72K771HNQzf+t5vo8FxZoHnPVfMVGm0K1AslMT5TcoKXOmt\nodnochsSFJxhZ8+uwhhayzvrXbz/+W0AwOPjOTrM1F7aBgYp9T1a6WCT92gJWSVTwzcGMZNAttst\nNJmJvD8eY8ihFe14AzGTRn765U3cv0cX+A9/d4Cm/xz0D0a7fSykwNxlfCpEfO6vrKy4Kgh7u49Q\nsBVioRykKwLhzoYkTR0RpZDSKUSU7u/4vlGt8bLQi+oksnBs5cYQ4SZANC6nF53vLQ/XFtZgNCBH\nyt7jr1CWNIavvvY2tt4i6qH21gZ+/Kf/CwBglmZ47VWCNTtN6drj+74r3G20cW2zwjqHQLfbxA5X\nvEjSFCWv2ziO0e7SPPa6bbSatB6kDFydPgHjKokIT2DpzYga5qulllpqqaWWWmp5IflGPVNq7Qow\nZF6neAPNNvFpQAtUppm19imor6rFJK11XFQGCpXjT/oRDNePKgRgJFthxqKqr2FlAWs4O0haSM5C\nUNaHrgK1rQFQeXcklMu0ERBy+cDeNCucJ0gpz3mmTpOHSakglFl8z39rIGBs5YYXp56XT9WbqjxK\n1lpnTXz9excMfiobQ2uD0vwaz5RZznNT2gB6WNUSLCEDGm9VGsfUaU7VNpFCuYzFtJ9gzlXQ/YaH\nsmQ+I6lw8QxZmRe62zhhYs+TowSzMWVbbpxNEXUr6K0PybCapz14PAa92Hfu8jLoghM+MZykLpBz\nGfGkwJAJP/cOHuI771Hwf291FZMBWfyff/Y5GuyZElGIJsOnwvPQYmtydbWHlS55EcMoRMCB9YP+\nMQ73KTC9yGZoMmmrBwErKkJO4bi/AhjEHMBtIaAZB9LWVnGxyEqL7DlKH4zzuctW09YgZCs8VA2s\ndGls/9E/egup8x4XUFU5nyIFuIakrxrwPfYuhSWEJg+BjEJY3ou69CE4eSAyvssa8jwFwVC8MQaK\n90QjClDw5K1vSpeUAaMg1PL2n/CEMxd1bp03KssMlTsBwXlVLTwlhUvikL4ATyNCD/D4RcYY+DxW\nDVjce0gBs2fW2wj5OPVtDoEFmW2VVWxTgYK55gpPuNpjfiChvAr+WyQhP0uUEjCmIgT18Ef/zu8D\nAN595w0cH1C7+scnOOJA5Ml47CDrbquFzS3mq4tD7O4SceznH32OfQ78LQ52HZmk54Vot8lzuL8/\ngKg4u6xBYWhdeKHE1StEMvnOu6+h3SL/XFmkroSMEHLB/bWE5GXpuJCyNEPGUL+CRMhIQqAESvas\nSyVdosGNi2vobtD+e3TzMVpMairbETrnKDGod7aDN0LKQCwM4PG5FUkfmuGhokwRcHab5+9gMKEx\n6fR2cPTgCxrDALCKPDWH/RO01OHSfUzTzHE8laXBbE57KAoDNJgA+srVK/jiAcGRX95/AMHtLIvS\noRZZnrv1kBcFtFkkU1QwpdXCwZfaaJcUZQycB0cqA2urjQ8EDLeVsIsEeSEQ+st7wqOoje0z5AG8\neVNjxll7v/zpX+OEk3pOnjzA4T6d93/ve6/jjZcIbm41YseP5inlSi+d9v0VpXZ9Jyi9IpCWru9R\nFLq153mLe0mKAAtnqVlA8ZBunJeRb1SZGhU9iJKwalWswkiaDGm0c3OfpgKzkJUuBRgLw5k2FgZV\n0w3gmISNKBfxOsZbKGXi9PsXEJtnJStdgEIOiQo+UYBl5QXCZewsI3mpFzAlFli7eiql3bjAFyXk\nqQ5bFy+hpHBZgUEYPKVMVZi6EMItdOV5KBjq+3r9tgr/NsY4N7A22ilTWpcwS2aCZVmOZsH8A7aE\n5oNLKc8tSGM1hGGMXjcgOU81S3JsXqRMkt55D8dHnKlXSpzfIsWkd3EFDx7Tgv/w431kE57z2b4r\nrqvCEiFTUk/K0Cl0vXbbFZI9sT5S7nfgeUhnyytT119+1Y2ZlREsZ5C1N7ZwmVN5pmmJKbOtt1fW\nETBrcaKBFYYTuhtnHb4fNxqYM1Q3n42cohe0e6i46A10tTRhjIXhrKogFvCdomQXRZClcJe2NgL5\nc7ik81JCsEIkEKBg/o9Go4scdOk0ohARr6881zBgiDPwXDaqgkDEB74nTlEXGAXPZ2hdRVDM3hwU\nGpaz2DwFFKwoeV4TltmzhdbQtiJHFS57tEjmKPTy8wgrkM9oDJNZiaKCp7FAKySEi31THpyi5PuA\nz9Ce8oTLtpJGoagUGAhHp5KUBs0GPRNKz2Xnlca6OZLCYBGOIVycWl6UkF5FuWEh/eXmUetFsV/A\ncenizPY2eh1SItSNK84oK412kJCvJIJKwZEWhm/YH/zwB/jqiyo7OsDNzyn7+tbNW7hwgeJIN3pt\nTDi+VCiBdkxrYXtzBT1W0Kyv3KUkpYRXXVxSPhfMJ4Tnsq4CGbpKA1EYIuAsxSiMkTGMXOoCmg2S\n0JfYYB6DC2+8hnRA58SD/Xtob5Hx1u21oXidZvqUAlJkMBwbZczcGeOhCNHhNE+T9FEYigP64Xdf\nwXUm21wJS8zHy2eAl7qA5r2epBlK7ksBgyKjtb+/f4CPP6VM2bwoEfLdoLGA4YzRSKpipnaB5iVJ\nAsGKHoRFyc/IU3GfAhI5r6WiMA5qFMpHV1X7GCgrzgEjMBovR6dDf6vwKlc1yNNv4+c//zkA4Hjv\nFgaHpCRePLeFP/wdMlwvnN9ybP2AcMom3VlshPiLuLSiKBdnNoDKmNfGLCpPiGBB+msUrIslXmTt\nWbtYn7q0WCggz5Ya5qulllpqqaWWWmp5ARHPRfRXSy211FJLLbXUUstTUnumaqmlllpqqaWWWl5A\namWqllpqqaWWWmqp5QWkVqZqqaWWWmqppZZaXkBqZaqWWmqppZZaaqnlBaRWpmqppZZaaqmlllpe\nQGplqpZaaqmlllpqqeUFpFamaqmlllpqqaWWWl5AamWqllpqqaWWWmqp5QWkVqZqqaWWWmqppZZa\nXkBqZaqWWmqppZZaaqnlBaRWpmqppZZaaqmlllpeQGplqpZaaqmlllpqqeUFpFamaqmlllpqqaWW\nWl5AamWqllpqqaWWWmqp5QWkVqZqqaWWWmqppZZaXkC8b/LHZg+n1hgDAJBSwhgBALDWQrJa5wcK\nUtD3MBZeoAAAQlhkaQoAKAv6ewAwxsBa696pFD2vtYbW+tT7pXvGibWQ/FOe5y3+mxCw/FEqBXB7\nxKonntXH//i/+Id2liTUBlgUJgcARI0IngoAAIPBGOmU+rLeWkW32wQAFDoFLLU5jAK0221+q0Ge\nZ66dF85fp291gFu3vqJ3Dg/ge9R33/chBXUgyzN4wWmd2fIzAXxJ0z8bzxEEMQDgv/9v/+lv7ONr\nb3/fuvEQTz8q3PcAeE600TA8D8Za+B79ppQSAvz3QsJo454vchozeaq9ZVm6+RRCuN+iubdP9Q2g\ndaGLkr41xv3t/v79Z87hf/JyyxYTmp+4qXB7QO/tehJhMwQANMsZfG6DERYnBbV2WCoE/H070FC8\nprS2KHntF8bDE68FABiFq2hoWi/dtA8d05xvv/d7WFvbAAAoAZx8+SsAwJNf/CUeFDTwDwoGAAAg\nAElEQVRXNm5ASIVq0HVeAAAeHRw9s49/8l/+Z3Y0Tbg9FlrT30opEIfUR09I12YDCU8Jbo9FXvLP\nauP6KJWCtfR8aQBjnp4PAPB9BcmbbjxLUPIzeVG4fWYF0B+PAQDTaYqsoLWfJDkst+F//T9/9sw+\nvr3VssKntkVKwjM0VofDmVt7SghIXqulkLC8hhoe0AhorRprMSi5nb5Cl9dSCIFxRM8I6aMb0z4W\neYF0MqEx9CQKQ2NrIRZnjHXbCAKArObRCnee/XRv8Bv7+J/+h39oFe8SU5QwJU2KF/qIYjprhDBY\nWe0AAIIwwMFgyO31IAW1PctLxBGtx/5wium0DwBoNRQ8fiYOmjCa5kGXGVrtBjdeoyx5v1qJwPfp\na2MRKp9/18Nc037SRiAtqFv/9X/zz545h3/xq/dtP6Xxm40nkLMpAKDIJ/AjWqeb22cxGNP3SV4g\nbqzQbxUSszHNQxQARtMz5TyBz0tTComgQ30pJBC3ugCAS9euIm7TmITSh6Lli1xZmFPzVh05wgKS\n15SFguT1fqWz8sw+7vzD/856vC6EEPCEz21brAVjDPKUxt9aC/dSaxHwtVSWJXI+O7MsheW7RFrA\nF7y+fIm4Q+eH7xl4gsb27M45tFd6AIAzaz1UV0ZWWIyn9LtlYSH4PUEMbG3QWfVf/UfvPrOPAGx1\nBv//JvbXfC/Mr/sP/D9AKfXMPn6jylSaaFQ9CkLffbbWVHcvtNaw7pIWznUmrEWl+QixuLg9z3tq\nwVWH9tcv3OoZay0pSKDLWslqAwBFQQtLeQpCee57VO9coo9v/dYlpPweKImSF1BRahQ5fW4cSAxP\nRgCAduTDWDpo5unYHbCBAnJDl12eAiVf1kKWOBnSYZdnwHhGl85sniLigyxJ8lOKjQAsHTq+5y02\n2yx1imea5UjzZXpHF2b17l833lJKd2H6wnfP6KJ089mMG2g26bBSykM1ummaYj6f03gkU6SsQH9d\nIT7dhuoCr577u2W5/gFApCwkDSW8UGEa02G7n+R4/dwFAMCToyE600MAwEbDYjukuV3VBrt0fmN/\nJhH69LsNZRFYeqkVOS4WAwDA0GQYRHQoeVDQp5op5OIfVX+VWPREG+OU5sUqXk56cQxf0bqYJymC\nkC4RpSSavI4anu+UCyGFMzwKYzBLaR2VaY4goItbSoks4+9BSgiJhJQ0R1EjcHPaazZg2WrRRiNl\ngyHPC1xcXeUeGcwz2gdZXqIZ+0v30fMkRGU8CAOfFas4km7cmlGABl/KhdVOAbRZAVXtIQU0+HkZ\nBE5JV1KhFdEeunZjB9evXQEAPLh/DzNWBi0E+gP6XBQGSUJnQ5FrlGZhHFS/BWucYbFM/wJWWEpY\nFHx5tloxtCndu5OE5mQynSFq0lwVZYmI+x3FAYqSfrPRbULFbJwigyno+2k+Q8ybt91puXOq2Wxj\nNKH+HZ4M0eI93W40obkfeWbR7nb4dw2SfLZU/wCg2T2LE0FK0MMHR7j32Wf0zukepKB2rq5vQylS\nEJJcY+vyS/RMISB4Q82mfXzy4U8BAOtNhZZP5/tkMsPZnUsAgLXz5yGG1OY9/Qivv/MWAKATN1Ed\nMdpTEJVDwGrSVABAANLwGjESIRsny0iZZ9BPGaZ0/pVl6QxRKSWEXvxWWbKBqkt4PC9KKXf++ZGC\nA55K7e4wbS3yjA35bgNXL24BALzAwySnfXbvSYGGz04JWEzmlRINRBErnpMcx7yugXeX6md13/yb\nJ6fbdfoMXf7OAGqYr5ZaaqmlllpqqeWF5Bv1TJFngb1I2pBvFBU8V3maFq72UhsoNgk8X0Ja0iBN\noZ23AwAMW3jG2K95LBZapsfwkhDCafsQYmHZC+GeeUohPeXVWkbmeoRCVG32wR5bBEYgbNL7/XgN\n2+fJ8vagUDBmMpnEqEygViNAMiOLcjieIiFjBc1GAKvo+6CpsLFNrtneSgshe9OM1pixdyfLUoQR\nNSKKQvg59TdNU+c1s9LCquX6KIRYeEwMfq3yLipL/pR3RXoefPYubW9t4ezZbepHEGEyIWt1OBxg\nNosAALMkwkn/BACQMHRataFaR0Is4KTTkJ8QAlLRbxW6fMp79SyR1joYyBQlzl5/GQCQxx1EEY3Z\nxrlL+NXPPgAAjKZDbPo0h93Q4hKjs5PU4Jg9NceZROBR25qeB3ZoYEUnCNiNboREIciyl2LhfZOn\nvX5CuDWitXYw32nP4DIyTBJoHjdjM2QZWdLGaCRsqTXCJsKQxrDMU3iK5sUKibRk6EoI5Hnl0fUx\n4/5alADDTkZbxAGtwW4nxDxj+FUXQAX7+oGDTbOyhOF10wwjNHvk1ej3B+gwHLWM5NpDzN4oa43r\n7/ZGG9dfvgYA2Lm2gw7D7Cf9I4z7ZG3Phym6HdpbaTFHyVb7+uoGvrr/BABw685ddEDenTNnNnD5\nBnmmehs95xnSukQc0fsf3H+Cf/Uv/zUAYDCdI2Gvj5ACMXv3PFiIJdeqVBaawwgazRCSIehcZ86+\nzrMSKcNDrXbbfe4P+mi3qX9h0HDeM6MCbG7R91ZLpDPyYuRFji7Pg+95sJbmR5cGEc/J2kaINKHn\nC22heU11Wk2MhrS/40YDSi1vw39+8zY+erQLAEjzDAcDcvvOBkfw+P648/+y92bNliXXediXmXs+\n453vrbmqq6onoLvRjaEBggQJiLZIRZgK0SFKflHYCv8BvsnPfrHDYToUIT84HAqHZYeDZlgSKYki\nDZEimgBINLrRQKNRPdVcd6g7nnnPmemHtXaeW5rqlGH3080H4ET1ufvkzp07c+X6hnV/DxI0fpev\nXkO2+xEAYDTN8dKNzwEA1vvL6GzTczB1jllF45bWGne26XlOVYTWapuvn+F5n7IwhQpgLWf6rIDP\ni7rRAgwYQCsBxWueLwQ8s/iekeU5PDV/j63gdRkGimkuUggonstGG7fsBmEAn9+VKIrcGlDVFUqe\ns0IKNHCw1Aa+oTnw6o3r+OLnz9M4zCZ455N9AMAgldAMHXZ6AVZW6b1XKoTv0efRIMfJSZOZWqw9\nyz76/1uzzf/N+2LtPG54YuvHfO9fJKv2mQZTUaxc6t8YA46BGOKb30azOUpPQfEiDGEhVcNPsC7V\nboyZQyCn0pzAnB8lxJyrcHpQlJQOujh9Hf4juoZS8+BrgWaUdP3PtQGYLyGsgWLOlAp9CEX9l6JE\nixfBuLfkxsQXGkmbJq4X+khnzNMIffRW6DpRHGBlnV5+Dy0E/MLMZlOcHBMU6AUeZMx9E8Zxloqy\nQJVRP7Np2ex7T23iFOdMeAqyweKNheJnGPgBVJMmNhq2SYsriYD5OJ1uC8vM5fBUAKOpA1nqweiG\n72FRV3R/la5Q62ZBm7+YQswDNmOsg4ullI7LY4yGtos/Q+UDLqOuNTKGZ72lBCah+bi61sLnXnsR\nAPBHb72HHeZ1XC9LbMT0/W4CBAH17SS3OMrpooeVQY9uES1fIeQfK00F0QzvKSgTUrr5qKQ4tRBY\nd4+e7z8BET6taekhrQg+MVWJ0KP7qqoKk4o2xFFZISoYQKw0pGTIWlcoi4IHS7g5oTVtutRPDcFc\nmiLN0Gf4pxUDe9u0OdZ1DTSbiBdA8QKeVzVqhv9iL4CQ9NyLosARb6aLtIfHE7Qjfld8BZ+v82tf\n+wK++gtfAQAknRYK7ufq1hoMQ5Cz0Qx+SJvp8sYm8oo2pjzNsPEiwUgrtzZRTClIWN3YhOfTZn3+\n4irKmq6z1O86rmeaFdi6QDw4IRQq5mEVZYGIgylfShcsP60ZU8MyPIfAw2zGJy5l4TGMVZYFYoaR\n47iDwGfeXi0xYKpBVY3QIIu9vodsRP8uUGClQ/yjk3yA6ZT4VknSwnRM4+Gp0PHqgiREwpt/Nh0h\n4PXak8BwQOOUFxVUuDhU+/DuJ3jnB+8AALqdDsyU+hbKsJk6yPQAdUXXT4fbmE3v0G9VFj8+okDJ\nE4AQ9PyjduQg5c2tVVTMHZ3OprAxvRNvfO3L6PPc8esSPi+QUteYndAcrDQQnCOYrFCqyQ0AsLBy\n8cBhZXnZrWeer+Z/K+b7lZISsU/90Vq7NUBr7YLvMAgcVaU2NaRkDp82qJv3Nc9wvk/z4fpmCxe6\ndJ2LL97E3iGN7bDSCJv5CB91Qe9iluVIZzQH9nYfYzadH3CfpT3Loe//i2Z4fGh/aIKjOdR/mnVL\nY0vNf0ZY8gzmO2tn7aydtbN21s7aWfs52meamRLSwjAkIIR8IqXWpH6lFGi0CsL7NyLDU6TzRvGn\n9amMkqBMCAAIK6EYAqFrcyYDxhHZIS2Mnp8gGtjL8zwHUz0Rti7QDASaw6JQ84xCXdfu34Haqd2k\nrKHRqA4VlGygRo0goj6sb/Ug+FHVtYVgBUalC4BhhtpQCpp+t0Rt6aSWxB0Ey3QSzKsKPp9YlQ3A\nIjIoX7j0/1ObcF2HkpLuEQCsQ21hrIHhm7XWOljQkx6kajIgBkZz5tCbE9aVko7caoyen8yUcs+2\n+T3gSXhLnFK/AHAwnx/47nSySAs84SA5KQHBhGxPhWgUcNPlLs5fugAAuLz2EZRp1HweDifUz5Uw\nQ5+HZ8kHmPuLo8zgJKf7GlQWHSapB0ICsknrKzdNKbPaZEqFU375QeAyfUqphTMaAJBVKXZPDgEA\n5ajAjUuX+DoWMWcUyqpEcYpHW5Z0Ki3rCpZPwG0vQM2Zu6I+lVlTHlImqUdxhCChfk7KFLnHyr4w\ncqqnui4QMoQQQeKEsyyRsJBmfpos6sVPw6GvUPOYjLIa66uUCX3u+Rtos1J2OpuhZGjS6AqzMd1j\np91GkFBKV/ghOgmd8sM4R4+vv7a2ioozce1WCx73P4gSeH6TdfVJnghgbW0Z/R5lu3Yf7UFKnhAS\nKJmwXGrhRCJPa0YbBDwXsjRDUTfk9hpdn3opvQCCs9Faa5ehCIzAco/gvNEkR8okdV3m6KyuUF/y\nOXE5SSJoy9+xFlWjwqxrJDxOts5R8HUCz0fAv5tlM8Sc5bESKPLFn6GHHCofc99SmCmr85Y6bq61\nAgnDa6jOjty7IjKD7TuPAQDKCiQteo+LjQSiUZLXNXprRDd45Y3XsHaRso5Xz60hzFg5eHiAwc5D\nupejx9h7QNdcPn8N15e+Rdfx2m79k7CQaFL94VPvsZzN5kIo48M277qUjuBuBdw81TDuO6fpLrQ2\nzykPVbNcWgGwEnOzo/GrbxIc/dxGghXuXsc32OrQXBrOPByO6fo7h2NUvEdOjyc4OSDaRZ5PEDyD\nGOSzaKfv3Sn8hZwLl9Qp3bc1KPjdHYzH2N2jZ3r/wX1HW3jlxRdxhdfFJEme+vufaTBVVRoF8yWE\nVO5l8z0Bjzk7UsEhflrXgGoWHDUPvoSm/wZaIJxaTBho3QRNErZ5YYRF4GTdQM2YdG0FZIMlS+Wk\nrVVdQXDe2/M8t4kvksaTsnSwhJQGDQpmISFE87D1KYw2QOML4VnheACeNG4RVsJHFPT5c4ROizaC\nMAzgeTw+VsAw9FmWGVrM95ilI6QZLUBCSZQF3ZfyfAQJ98G3iOxiKU11Ss1nrXV4mJDCSZU979Sz\ngp2/8EbD9+f/RbFkbnl5xSn4giDEdEaBoDFmzus6FSQJYC6j/zdw+NN9My74Es+kJPGUgGpS7VJg\nMGRowWsBvIkMZxka+k4/FliOmE9kND6W9JK+dyyxqejF3FQlAtZjr8UKMT/nYSUx5GdSQaAn6HkG\nno95ZHLqvgA0MmovjJ6AsoVYPNE8mYxwckKBgy4ESl4KJlmGVszcO884xZk180OINRqG1XnaryE5\nQE+sglPoCkCbRiVncVTwMy2AnA8MdVliiSPM7vIKllfWAQCRF0DfuUf3qDz0YrrOuMzQfgaFzRtX\nV5DxO5HmpVNo3tvex+GANsq7d++hxWpNnY/R79BvffnNryKIKEgo8gxp1lg7eOh2OVDyPDe34yR2\nUJk2FuKURUu7Q4HYlfg63vgSvYt1pXB8MnB9Pb0RLHqH0+kMW30KfGSgoHgOTmYpDNiiwBMuoDQw\nAEOO3TiA4iBoluYOtm3FAtPRCf+CABp1Xr+N4Yjmi/JDtDt0/YeP9px9Tb/rU/AIWqcmDMkpabDO\nkD6kxXDyDMGUMfBSelbpNIXi9aDIptA8B4PQg2QIbHn1IqYp9bPIR4gSlvILDyHDyDLPHTQ9ONmH\n5X+fjPbxyutfpN9NjzD+yccAgAd/+TaOdx/w3w7Q6VKgWsy2sb9B99X7yq+gbtbuUwfqRdrjO3fn\nAaCYH1B933fzQuuaOIagJIMKGYaLQgQxc5qkdDQa31MI+QCgPIXlLn3+m3/tFXzlBYImUeUoGbod\n7m/j13/hZQDA50cJ/v7/8n8DAI53hk6he7S3B9Mo3oPQBdSfdTu95s+RVfvEGJ5uFa9hk+kU+/vE\nC7t79y4+/IRshe5u72B/n5TZWTp1As2VbgcvXCdu5d/7e//VU/t1BvOdtbN21s7aWTtrZ+2s/Rzt\nM81M6bp23iPSirmCzMJlmoA5jGEtiNUKhpB0Q9iskHEmA0K406FSwvmuWCOcUWNtLSybcSSh7yDC\noqohTANT1e46xhgEQeg+N22RpKbWFWpOH1pdOohLSYXThqWnT/DuN7SA54Xc/zZi9v5ZWbqIfm8T\nANAK1hAH9O9BECCO6XQZhaG7rzSbYsAQzv7hNj59RATOrBg5c1RhNWzzLIRBGCw2FXwvcMq4qqzm\n5HKhXJYkCEOHBVZlCdsICpRAxGR75QFpQSfOo2OLvGxUQCWkI29a9wyNpewe/evcl4zvhv73FMxn\n7WkIdy5YWKQJQUJF+lOJGUMLaE9hmOU9PDmEx+TsygbOqqTX9/Acqy0rVHhwTKfwHS3RYo+hLa9G\nh4npiazAUxbDUmAypOeWD3bQX+VspFTutCqknUOfQTgXSggB6y3+Ogd+iI0VOqFKT2JWEZQiA8Dw\ne1kUlcu2tILEGccqpeYZXS9BzEajeZY5aDLPZhCqme8EKwPA4GjkxqqdxPD4hJ1WFZbZOHZcV+j0\naI6XusKsMc4LEsh/r8Hev90O8go134AVyi12P3z7XYQM3U6GE8oCAujEBpe/xpmJIELOGGfgW0im\nHKhT1AOlJALOiEgop6yzZu5XpesaklNifhDhlVe/QOOZdPHxT0kNOh4cPkGAFQvyCgLfQ9KirJrR\nGgVLfqPAR8wk75muMJvSs82zGRIe7zCMUWrKFnbbAp5giDUUWGLVnufPVzxdFvC5X7GSqDX/ltLQ\n/B6nqYflJVqnyoKUewAQeEDNmJPypaMaLHSPXoCas2mwBmVDBDfztWE2q+HzOnj8OIO2rDTVmENR\nZYWI55fWc2UnpECWUXbmR9//U0R8/S+8fA3JY8qOZh++i1V+//pthc0NyvL4rQofv/8WAKB18SqS\ni+RBV2sBIRa/R7+sMfdctE5IYk4RtU/7yInAnzswCgUrGP4T0gkPlATKKT0jIQWGltahlW4LL79M\n/TSlgan4d41Gyc9oZ3iMaUpw3v69O6h5ry2rHJZXxrpqw6ueDn09a2tmvsHcwgvGwjaoDubuk0LO\nveysBcqc5vMoHePxEc35Rw8e4tantwAAd+4+xNHjIwDAJJ0hZ9i6Mp7zDlN6hkDTHnXv+DFu3fop\ngMUyU59tMKXn8JZScyjIWOtMSLWwkA0qVBs0q7nw4DZuXdfOeNP3fWdpoGCdxHtWljAMk9XyFH5s\nDJofyMvS2S2IU0GZEBKqsbx9RtLUeDTClE3RDDDvp+e51LIUAnNKloFlfW01M/BAE3Rt5QY2Vp/n\nL7VxcMD9qQawejy/Do9nHMXY2jxH91sX2D+g77Taa1heeg4AcHR0H3lOL4kpUyfltUailIup3Yy2\nLjjS2p66D+u4HqclpcYYBD47FV+4gq1z9CJ34wQeK/4KUyDosYmiUBgyzCfkaUsA5TYZY4zrwxN9\nO+V0boyB5O8rKRfeoOgGLMB8Lqssdnf26PPJEBFDrC+cfwHGsJw9jlBLeubK86BYqXl5cwVxRIHY\nYGYAVoc9nE7hjeil3ggqdBiC3ggtBilBI5/+6bcxeEg8jee+9FXHpbPVKV6YgJNUQ0qIZ1CdKuFj\nbZkgnNLWsKxo67UjHB2xHUWaI0kYnjEBBCsiu+02KfEAhCpBqOi+Ss8gzVhibzVKdi4X8DAYMZ8n\nB6zH3MSWROMr7HkR9h/R/Z6kY2fCaI2Fz9eXQsNi8Xu8dmHZcVE8KdyBIQyk4xeW/QCWA+24HWJj\ng2AzbTQsR32e50NyYGGtwixtjGSFU2vOxjPHy6MAsTFbnK8rZVm5AGNjcx172zS24/GRU2VCiYXn\najuJkLP83VQ1WhwojdMMmg903VYC1WyAxoDNxzEuUng8T4MAALvDJ+0WooTmRVEUzpKk1+0665XA\n8xAuUd91kTnlrhcFzsC1yAv4PGZKWlg2mSyr+gm7lKe1UTaEDRg+CzzEzDlrt1YxGtK7lacpmuXr\n7q1PELL7e39zGUmXjWnrAYqmD9J3rte9Tgh2jkHXt1gvae4ve6sI+vSdC2sxunyULlAhYwXnWreL\ntTGtDftv/Qtc/uVfAwC01i88AdE/tenSUVuUnFsDiVOO+UJIQDY2Ip77ju8ptEN6V6qqQspO8Nr3\n4DPsG0URPKa8FNMcHhoY36LJJdSQqAt6dxPM8Ne/9Tr1J5/ie9/5Po2bBjxew/pdH1WZLn6PizYX\nHBl3j1I4xgusNtBVM5cqqpwA4M7de/jeuz8EANy6/xG2dwlCn5wMoHltM/DmyRkYKMmHQ+1B8HoW\neyMsMfw99TVOhosbzJ7BfGftrJ21s3bWztpZO2s/R/tMM1OnfaCEmJ+/rDGOJEaW+Y0/EJzaB1K6\n2lNKqbnBJk7V6dO1g2esFO4k8sMfvYfnn6PsjL+yTPb6oNSzYQWMUnOYSkrh/DqAZzMbC8IQCWcL\nzKkTplLKwTNSSndwMUbDcordqwO0I/KhuXThVfQY2ktnpTuRC8+gqtOmY+7UO8tH7jtR5DsSph/1\n0OuSMVuZ1+gyJFOkR6iyJn0u4avFlBm6nmcXpZRzgzlrnR9XXc2lfSpsYfMS1RK88dIbCEM69eoy\nx/IGGZcKpfDoNpEBT6ZH0F6H/1ZDKrpXKSwkP93TuYnTRp3GGJcx0Vq7mmtGzs1ZF2lSwBHQSwMH\npU3SHL02nVpO9veg2Y8pCRVabS6XYSqwFyOSRODlNXqGP/lwFwVDBd1rN5FPKIt4MDjEwYROw+06\nRY/JsF5V4PFPfwIAeHz/HrrL5Pdj9FzlJ/3IZTuFNfOSJAu0qvTmcLSv3d+WZeZOh8IIlFkDmRSI\nWZEn4CHi03BtMudvZG0NYWhMPK+el2/xEnxym/x+tvfHePUaqSCnU4OQs5a3PtlFxv3ptAUkq1HD\nsI9eh33bigKcfFmonV9pn8o2w81JQSoUAOQL15yA+8sJ/JD/3ZbwA3qmUvrIC5p173/wPtotgnle\nfvlFNGtVWVc4PqGsoq4Nts4RmT5MQngMBQo7Vy3DGpfJDU6ZLUpjqHTWAq2VRMiZOJ6EAaImu9VK\nEPLpejKZwmsMUJM2RgzZ5HmO/lIDz9YAw4IrW5cxHNGpPqsAzYKI+3tHWGvTu9vthC4jAM9Dd4kg\n2Va7hZpTX+kshR/SetdpdRA3xPTA4mh4vND9AUAQKiRcS04ACEJaK0UrQRSyZ97jHLoRrZQz1KzE\nVb5yKlJ4HadADWQCxavI+X4LV87ROnS+m+DmMl2/L6Zgr1X451dQjmicR5WPO/tE0M91hiXO0D38\n+Mf4qab+vPTNX0NrbY3v4OnrqhZ2ruZTc8dFqeaZed/34HHWT0O48jOz2QzZiJEKqdxcziYagued\nkgJBxXOzrDDfX42Dzzwh4DN0Wz58B7/+Inno3f9ZiI9usV9YvIa4TfM6y8c42d996r0t0ub7q5jX\nq7QVaieoqjFlBWg+S1EwfHkyHGH/iGgRf/rWd/HdDwjOq2wKzzJapTU8phDV2sCArtMWBv2Qf9cL\nIHP6zs1V4Bot2fjZTOL2w3zh+/hMgymLOT/IWg0jms3OQnDaVWIehEyzHLdv3wZAtaRef+016rQV\nmG+gp1zPrUbJG3pugTu7jwAA3/vhD3DhAi3gtbanUn2nNGdPOJ3P8Xjg2WoKRVEE0Tw8Y57gtMyN\nF6WDBIRQ8Dh4WN+4jPMbBO21extIuSZSVpanVAsaVs65Xe460nMBaTbOHN5+eDJwUI0vl/D5F2gM\nD3bu4WiPzBN9z3cp+ac1IebKOOUpaHYGtta4awgBx0mIW6tYvUgvZqu3isNd+s3hyRCCV6u8trj9\nKfETBo9uI2SYwZM+IGgxDFTo5MylLecFeE/V5nuyBuNcCVjXdl48e4EmJeCHdM2slM2+C08JrLVZ\nsWgrZ58QKDiLgihso8Ny6MFkgtV1CoIuXljH/hG9yNlsABXTM1lpX4WtaW4ePt7FdEobWVtkUByo\nFrMRdrn+mfQkFG9SpiggWcljYJ7JtHM6GyMJGtm7h4ytMfK8hMcb/lLPx7SZg9UUNqPfKjKFkAON\nVtsHmLNhyxRLbGpa27mhq/IjXDhP4zCcVvhom5QzZTF3fk4zYH2JIIQLKyuwDLOvLLdgGF6cZZnj\nCC7SgihGwTwvKQU85jsZaxyX0RrtauGFcYDhgDb6MFlGu81BEAyGR6QCuvWTd3HzeXpHhbnpVpBK\na9y/S2aROzvb+NY3vwkA2Eg2YNi4t5W04DNUluWpU7CGUehqralTxaKfen9KQLbomcTSg+bi6VHS\ncvQCWIPZlKAfT0ksc/HeUleoa/pOe3kTmut3Ph7l+PDDuwCA/tISblyng9D04QOMc679148xYXVm\nJT3MGoVXWaHmag5CCWcQrGuNGQejepY7+Hehe5QWQnMhZV+hqGjMRoNtWFaGF4MdhEyt2AwM4g6N\nScdPUTEkG8CizXXlzi2t4folOmBe3uzBYz6RV6XYXKE1qRUo1EP+3cBHuUx/e9/D20sAACAASURB\nVPGVryFjCOm9d76DLbapORiXOHiPipEHF6/iakJ9QPL0+Spl+AS011BMjNGoWcEXKIsrmxS0fnR/\nzxlyhhAwYm5gHfI9BhFgmXsc+R6ub9K/X760BMPXNHZufSG9CJYD3nB5EzFD4r/0+k0sb9JY7QwN\n3voeQWmH23eBxcsP/lvtNLe12WvLssSU5+psNkKW0vhPpzkGYwpgD4+OsfeYAqjHB/vYP2R13sNH\nyHlOenoIj6HAUFq0eL+MIwV4NB+24hCXWGG6N57BsIHumpQIG+VxJdz4L9LOYL6zdtbO2lk7a2ft\nrJ21n6N9ppmpWZo6ozqBuZljoY0rJxJ5CmlKkeG7H36AP/7zfw0AGE6G+Nt8Qv3iy59D2NTj0wY1\nKycqa50R16wu8d7PiIlfWyBkL5msqtFwy4XFKeWgnZ8IhXS5VimUq3+2SLOwzt/Kl747ZVhrnKei\nqS2gOQUermJ15Sr1M01Qz+h0MK2nrgSKPG1MCg+K07dlWUKLpjq8gjFNHTIPPAwYDMc4d4lONL3+\nOg4OKfIfj2sETOyFrl0tsWdpUghHihRSwONT9+kSMlbHKGr6/cE0wLQkhVpatvDhBzX35QDjAyKT\n2tJACPqcGeuq2UsvRMC/5Xm+g2HLskRZNYRgg3+nWMDClaJZpHlKIGnReI9qibzB7ZTEyYR+y1cW\n6z0mNEeRyzpGcYKAiZ9xEqFK6dR75fwyxhMyhrMGSPkknRm492D13AX46grdrylRTtjX5+gYGNLJ\nLKs1ipT9ZoqHaPUp4xN0u89U41yKGTrsqZQVEwxHNOZS+q4/vlQYzaj/FSRyLodzMqgQhFx3bZYg\n4ZqT2axGJ5+br/Y7dPKLvRDnVigbcf5rVzHhckH3P93Huz+lzHOnG+Nz12huLC/F0M4zZoraNpnk\nAuOTxU+K2hr4Dhe0EKzoVVBO2OIZhZgNw6TUjmSfRIlT+RX5DGurNM5/+2/9Fjo9+tzpLTkPnl53\nCa+/Tlnf529ec3AwtG4O/yiKDFP+/vHxIaR3yiPM+QnphTPhnhBoc+YjTzNUDIOb3LgyN4AHw+vR\ncDhGFNK8669t4DGXePHKFLsHRP6fTgaomfpwdDQkCBBkONriTN1xmuFgxERnk+Ey1zk82j92JPXl\n1SWU3Add5m5OpXmKMHq6kWXTqukEOmdVmg3cdc4nCVZYyViXHSzX9LnnK9g2jV8VG4zKJqupEbL6\ntqeO0Z7QOyq9HL6i7/T7LQTsx1TPCky26Z0bpDUUw17hF38JF1kk1Hvty7CP79M9PngEe0AZq7Dd\ngoyeBc0InSeekBLa1eYTaKSvYRzjhRtkIHnrkweojFNuwOfstDAGZWOIamfod2h+vXLjefz6N6i+\n6OWNFiaDAf/WvESUJwM3B298/g08ukfz4Z0fvYexpTG5decQH/2Usm+eVRCis/A9nm6na+Ht7e3h\nhz+kbFe323UZq9FojMePafyPDk9wdLgNANg53MfeEYl3ppMhKs5UCgCWSz5tdmpcXqY+L/VitLhu\nauRbVCws6vs+jKHvHw8PsBZs8HUiHB7Q/N8dT10NykXaZxpMfec738YLz5Mx2NbWRSguGJlVBUYT\nwnTbge9k7H4cwjLv4iSd4Pf+8A8AkMLnCtcXGh0f4/x1SrtrY/Fwm6C93cEJPmaIMIkiHBzRRpaF\nMVrM02gniSuwPBoNMGSzuueeuwafLQpqW8NynbD+QncpnYw6Fh58VswYWFdzzpNtrHQJ2rlx8TVs\nrFEwdXyQI502368x4A00n6TQzAUSvo+wRQtoEPhO2qqkxMYmYf9xHOJnP/sZAGBjax0BQwGHJ0OU\nnC4V9RCKcXRltVPhLNKatKw2cJJ9TynnXK/NXL5aFz6OD5raThLZhBai8fEU5Yx+vy6PUNe8+GsL\nFIyPFzmqch6MOnPtUwFTGATOSiHNqlMqQu24P7BYGDoBqK5cK2ii6bl6kaBMVphIgeGEAoSNtZX5\n3/o+SpaoB75yxXunwwFWWD0npxUs1/LbOTrBZEKBjNE11laJa3H5+g1sXCDlYz4b4/AT4gPIvR3w\nn0LrGtkxQWbpeIigvfjittTznLNxmhs03I6y0HPlmoJ7R/OJxpD5OcqL0FBmHh8P0SpYwZWVeLDL\nDuKJwMUNeg9WrXFjooTFb/61vwIAOHppgt/4tV8EAHz48CFSNs6TZu5Yr6SEZDwhz3KoZ5CcWzGH\n6Ikyx++K582hcl0g5vcJooDP7247abl5XpUFfIZP+stLELw2fP/tH+HTTwnae+PVl3HzOXpeSSTh\noXHuF2j2PWNqPNqmTWpndxvK8T7NfCEWi/P7aqswGjccNQ/gotTZrHS2IEu9NhKvccvW2B/SRrp/\n+x5qhtCzwxFOxgQjx4GHgOX1oe+7um+D42OMxzQecdxGwbBI0k7QYhuLhztHGHPttm4QoMf8prrI\nYZjGEcURfG9x52xdl4icktIg8emab1y9hEscRExljU5NczMUGpJ5b7Ibw3IfTk5yjI9pDi55OQIO\ngvIDCcXGsWXew5DhfTOaoNih+Zgna+g+R5YWk1YfJSvAV174HOJrFEiuvTxCzQekk7rCEZtDojNf\nG/59TSrr9iGhJATD4x4Cd0jr9lpod9gGJ0qQsdGvNjls3gRENZYZ4nz9pefwza8SRLu23sP5JZ5T\n0wOkBVMnQgHNAUVdGcStVQDA9t4Q//V/8zsAgL/4wV/i5itfBgB8+nCChOe+sQr1/8vw4XQwdXx8\njH/wD/5HAMDKyjJefImC1slshlsf0rs1HgyQs2qyKEYQHGy2Q4EVDpw31tbwcJvG/5X1Ds7FzVzN\nITyu1uD7QEXPOs8l7h7R94+PfKDFhxlhcTKj79w7rgGz+Fw9g/nO2lk7a2ftrJ21s3bWfo72mWam\n/vwv/wwPdu4DAK5fv4GbnFEazSb40c9IuZTOxljfJDp9GLVwY4s+dyIfOqAo8dbt23jAONZH77+H\nq9ep1pApUqQMC9VxBzM28SryEb73vX8FAEiCkAnswBtvvIGNc3T9e3t38JP3KYV5NHmMi5uXAQBr\nq5uQcvFhqiugYtM4oUvUrCoQIsH6CvXz2pVXcWGTTg3daAWCT//9qyFqPvHVdYGCjSzzLHdk1bzI\nUTEkV+kaJaeH8yLHwQFl31qtFmKGmiqt8eAunYZHx8fIGGpaWwEUKDMEq+Hbxe7R8zynmLPGkjIK\nXAbhVO1EJ8uwAjP2vMynBsW0IVUXsGwUWeb3UNZEQBfGonD3Vzq/FimkK4uhrZnDiMC8rp+noHRz\nPvBcfcC6rvAsikzPs2C0BxrCGcNJIbG1TNm/117cwO272zwO+on6Z46MHvVdvS57PMAkb7IzBv0O\nlzApKyRybkZ7/JiueXywjze+/CUAwKtv/iLO+dShe9MD3OcMwaxWSFiVpEwFO1lcJTUqU6RcQ9CP\nIviNSgoaPS5DIbVFoZgs7BWYTgjaCaWAYGKmLQrU/H6ESkBwPck4iXBwTFnQokoBNshb6bbRYpLy\nhc9fQ8pigx/8T3fwkA31Lq62kPOJsNdNIBWN59r6Mqp6cbjW930ozoL4/nxOSqVQFvQsDOy8Vqf1\n5iWoUCPlbJo5Nd+KrIDHAoDdncd46zvfAwCcW1vDzetshmgMCp57SRA4PZf0PPgM0UNI+GwMXNel\nK2UVeoFTxT6tFQgw4/tYinvQYCGAHKPx4JW2RBw02WMf44qySEfpEfqr9JxFXsOAINkXnr+O8+ep\nVt1gMMDxCT3DtbUV7O5R1r8qawd/rK1uOLFJu9WGZkrE7OQEmlWeRZpig68ppMBgMFzo/gAg6bXR\nYxNRU9VY6dHnG5fPo8Wqw7X1PvoRjXeaj51yMOgESJg4PmhLnAQ0d1Z7AUzGpOdZBcGp3vzxBA0/\nwkwzIKf5jo1rsCtXuP8e4gZ2NhYl/5a/tk51XwF8+taf4+gBrWf/8fWXnnqPZTUvryNqgYYPIoWA\ns9PrAq02l1/zQ0dnsdUA66wovHJ+CV98jTJlX7h5Ea+/xPuiyGByGitfTxF59E6nJxkKrp/pt7oo\nPPr8B//sD/Dd7/45jUlV4v2fvA8AGKchFItrtPKBsPXUezvdTq/BTTa+rirs7tCa96N338EHTM3J\nTY6cIVrfVLi0Qvf++fNdLEVc+7Qt0FG8RxbHGPL4r8VdeCllFUPfAwv1kOceJpyNejyusV2z2GDl\nJnqrlNXfuNrD4QMusbM3hC8Wz0x9psHU3mCEhweEj97d28EBv1Q//fAWPt0mBcntO59gbZkAtc/d\nuIFuwIovKxAxnHJwvO/ckpOlJXzv+/TgzewEMbs69688D5+lsweP7uP2B2/T96PY1eI6mRwg6dIg\n7uw8cjXs3v3JD3BuhRQMf+Nv/Kfodqg/l/HcU+9RiMBJ+I2xCH2Skl679DquX6NUca9zEcpS/00l\nYPnlrFE7+ba1QMQGaXHUQo+5MTDa4euV0TgZU0B0/+F9bG/TpIyiyMmuT04GqMrG5bbC4QF9Jwx8\n9Fdp4attiXLBYKOxj6B7Fa6YsJTzgpJKShjRGKXNUBtalKxIUBt6kS0smsKF2mSoNX3H92IXmMSn\nilQq5c1l5TDOjZt4eAzNBB2nKMzzHBXDQ3VZ4ZkIRfP1DDWEsx+wUiPnzfzenUcA87CUnBtp1nXt\nFkDpWaQcSXqeRMJuzIW1EAw1V702pil9Z6W7hDar4QYnI7z9F39B1/FD9HkDH8TLCGNW6ZQ18qYo\n7myCzjPw3owGgoiNQFGgYp5MFLVw/XxTu0vhDnMnyiol23oAcQDHOYqlj5IDjbzQCBsXzsK4QseV\nlhDct+tbKzg5oGe9/fgudrkw7vbDPfh8WCqFgeFUfl7naMKR0G+j3XkGLkocu4oLFnDwme+HsE3A\nHviOE+kpDxV/fzwdQ7PUXQnlqAdJC+79vn7lIr7+JjmmX9xah+Y5aUzl1EFV4TujQymVU00q5bvN\n1wsCxwE0xrgg62ktLWrUHPylZYVZw5MqSyytUdAPo9GU3SwsMGCFlJfEWNmkNaXcPUZQUpAXxwF6\nfQpwB8MjJGwtYK12dfeKSiPhIDiO2lhdIWi6yg1279N8CfwADXEgWVpCxeqqLMvm9UQXaFYbZ4nT\nSVrYXKP1dLPfQTUmVZcVFRJW2cbdLpBysCMAy0FHoDTWV5ne0VYomCukrIRpDmOBgsfvgRUWJY/b\nwWiACR9UN/o9hAyDnjZfNsY4jmuv10PZXRxyN7p2a7o1BsrMC0o3dWFNbqiwJYDR8Birq5QEWO95\n+M2/+iYA4BtfewVSU/AbaI18RIeroAVYXieyfIpyxnzTKRBHNAeqUuJ3//ffBQD8r//wHyLguTmr\nK+SNItJrQTSURS+EiJ7VAb1ZI0tkvOb5vsJXvvQGAOD23Tv4lBWx1tOoWWG6lpT41g3a+zsWCCWv\nqdYg4LV5lKZQvJ/McovhkPelWYi04DXVSOge17Jc7uLl175C9x74zi7pZDzCwwOKRbwgQWwW3zjO\nYL6zdtbO2lk7a2ftrJ21n6N9ppmpC8t9PNqm6tsXVjrYu02k2kcfvo/RgE4Z3aqAeUxksw8OD6BZ\nsXEyS3H5IhG1V5ZWMOEaUNevXodhguXjOz9DvEonF6/dwfCEUn11kdJRHMBoPIDH379951OMON04\nnU7gczo8nU6w84BOWJPZCEtLFM2++au//NR7TGczZGyq2RJLeO018pu5cfWLEGjUcwmsYUt/2FNZ\nEzu304dwp2rWKtAnMa8Cf3B0iEd7u9zPievD3t6ey0wppSCbU4+tsMLeGrUZI2O4RfpzKOtpzYi5\nKkoI4cxTT9fFg3X2JbCYAQWlmG3Yg2rqcukAhsdD+cvwKzrpJoFAzXBMGMSwzitqnpHzfOkyYuRp\nxapHT7kMS55lsE4hY56lIhCsofpaAGCkcrCdqTIs9yijeGmz40oXheE8W1LXFRSTZCfTFAmbIRZF\niYQ9kkoJTKb8fKREjwm8BwdHWOGsrOf5CBlWe+d7b6HL8OJyr40ek6rDssbxlLN7fojjbPHyDnVp\nXGaq1sL5HJUooHh+tVtthJyZOLk7wdGI+vz8pZvw+WQ/mOQ4GRd87wXCJlNpLMBQV6lnWO7R8/WV\nxJ3HlE39l9/9EAErAbfW+5hOCOYzSrjTv5AZMlYoCSsRR4tDC1ZIN9+klE6PUJQVfM6OhEHksiZx\n3AYU/ftwNEWrRc/F6goeC2GKbIJpSmtVO5b4pa9Stnml14FmAvJkOkYU0bP2uz2X0cmyAlPOHnkq\nOOVhIxCw6rB+Bhgz9KjGKUAiEtkoov0WAp/G+3B0BD+mMZjlOQZswrqy0cWNF2g91UZg8gmtIzu7\n28gKWkvarTY2NoiUfHB44OaI74cYjegZjidT964LKR00KqsSK0s0Z1tRjBlnKQeD0TP59pVZjoIV\nsaH0nAdaHPuI+V3cvX+CKdMauksJDFjEUwNFzj580Ogv0ffDAGBrKRjfOhVkFPpI+J3QvkLNEM80\ny/Djv/gBAODlGnjh+Rs8DoHLnFvBKlEAL7/0Esrzmwvfoy1yt1ZJIR3to4JFWVNHdTHCakK/deOc\nxBuv0nN57cYann+OPi9HNSJ+7kIXAM8vXwD7A8pYVWUOpRkG9Xp4OKA98nd//x/jh98h9GbJizHi\nMaxSCylpbLU2kB4rlfsbkMlikqymZSmtZ9Px0ImV+t0u/u5//l8AAH7ywU/x3/4P/z0AYFKmaJQb\nfWmxZWlOispHxVtIVvoYp7Q+DbMQGVM87g8DpCW9u2HnAi5dYspOpGD6tH48vPsIH3L5qsHgAIdH\nhJJNxhNoVgi2AgXvGfaNzzSY+pUXL+H9ilKPa2aMmmX9X72yhu2AFqIYXbQYi/1kMMCf71LwNSk0\n2hw4PH/5HK5cJZmoimMsb7GKJg5wzFLe4TRDxXWrTg4P58UyBXB0Qpt7mpXod2jQK1WwOyywuryG\nMasLb338M4Th4kaBRTVDxSn+zQs3sLl6EwBgitAVGZaiAniyWswtE4SQ+HclCy2sK06U1jPsH5NS\n5PDkCFPmVR2eHOP4gIPHunYLVlUWqGY05tZoeKxWmU5zWFZGBYGPRSlFQkrXQyHEE7Cfc7e3cBCe\nsDlQsVOu3wcULTLSh6ud1xE3MNXUdyseQVjGqY14ol8lP580K1ExpFKWpQs6lVKoGS4hR/bGzE4/\nk5qPrDGYI1NZVAwV9JMQNae8l5IVlOyELcwc/uv2EpxMG65bDYGG+9N3dc78yIPHo6jSGnXJtQtX\nljDmzbYVB2hzHTerDTRzY3q9TVfHTaQ5fL6mrmpc6S0+T7WwKBrLB+sj5s28mGYYHFJQo1slGmR1\nmhbIGC5+7sIlTGe0OO+dTDDjDVTrGlLRYt7vtpxC6cHjkzn3QCvcf0zz8ZNHj3DtOkGKYSEdHCVE\nhYKfY6Q08rKxplDQs8UNH6XyoV1UL1zxYaONi/a1tsj4IKK8CCsbBHtoK1E0lhtVBs8n2KYsyYQS\nAGxtnOFnMdPw2s2BIHTBfq0rlAwR7u7tYcQWFFobVzvUGgHL88dYu3DtujLPEHKgX+Ypuh2aj7YA\nDo9ojRsXOdoB9T2t4aDXVXiQPDevXrqETkiBz6ycuGCk2+/hwgVaZ89fuIp//aff4b4DL7/0ebrX\nxMPohGEXKGhWfKIqnRKwrErn1O4HIfJ88WeohETezPGigneTAhnVSdD1qYrA9gc/cjBp4HvI2Jix\ntgrKp3diOJ65dVyUQFE0awOgmc8XSAHBOFar20HINgnlrWP85VvEjauswgobn26eOwcvatRtcJw8\nz/cQPoOyVpjKGUlDSlg+rGqrsbZG+9MXXrmML79MNJNidIQvfO4KAKDfqiFEzteZOGd/2AoDDpQC\nEWE2o2dx+dw17G1TsuK93fv4n3/vfwMA3H7/fVzboGc9zgz2BlzVQIaoG+6pFyBgxV8tfJhnOLwZ\nXWLnEe3lw5MjrK411T0KGD7MLPWWkMQ05sezqTuwhdKHZdX6aKZxwP3JqwDwmANYCIQXCG7eunIB\nM6a/VDJC2WEqz+AA+9yHw0e7SIumrmWFmrmnSwq4ymvelhGQzwBJn8F8Z+2snbWzdtbO2lk7az9H\n+0wzU3k2cn5Ck3SMowNKrbWTNi5coqi4HE0RspeFnM1QMGEyTjp4zHV49vb38Z/8BqkWpuMcmln5\n2fISuly35/DkBPdP6PtFrh3MY4xBlTckaIEJ+6vcuPYcNtcpsj04OMQe19lKLRxUs0iTUrlsRKe1\ngVlKJ6AsGzizujAMXC00X3jO08rC8c8h7dyMVAuJYkpj9WDvPk64tEg6nWD7wX0AVKOpOUFIBWQz\nSqmWeYqCiYhSGMSsCNG2RNmU8wnsE7UO/0PN87xTXk7mVHmgeSkXISSkak7dJVRFpwFVxdCSToda\nRq6UiDKbSPrkPzbLJignlPUwtYFt6vFpDc0Qnq4K57slLZzCK88zBEyS9j0POZetsHbez0WalKTo\nA0D1HfmZdJdWoVj1tj8yuHSVTmm2ypBwP33PQ3bYqMAEhlPq8/HoCEtsktnpBNiZ0TOcTEr4fHqO\nbQDj1ItAJJoxjFymZnAywMY6ZXM8KbHUprm/n4/Q9xfPSSsRImA4EgASzgafpCeo0NRXG6GpUWOl\nD8WKwrW1PgpWII2nOWacOVBSOpVXFIbIGUauDHBvh+bgV14B9jilHgQKfMgH1AyGy4ZU1qLmQbe1\nQBgx3DKZIJ0tfv4r6tqpYC3gSrkEno8a8/saHtGz8GSCC9cIBofnww8aM2CDWdqUpVFzD7XazlV+\nWiNgb6cwSpyHVJ7XgEfjEAaxyzAbK+CxCMEYg4ohGQGBYEECel5UzjQXVjrfsKLMEXHGajXuO2Ku\nHyrcnlGWWJQKg8f03K5evYhL5+n7337rLdx8gTIgFy6ex/YjWkNHJznefPOXAQB7uzvo84MTtsa9\nT0jUks/G8Dkr34tjBE2JKs9HxcpdP/BRPwN00opi6KqBwCxaLYaLl3toKco4RP0lWIYmkzBGGbMK\nT4aYFPRj97cHjX0dVpIY+ZAJ/3WNkJNI3aUQQ4bWjfXQiijrkQ7GONmmfSsfTx18bWrjlIwGc+8k\nQEB5z2D0rHMIVx7Lwhias/2lNr72Oinyvv7Fa5gcESwlp9u4xOrCtc0+RmN6b9qRheV3bjQaYDph\nQYLRKFhMsTtK8c9/SFm23/3H/wQYsplu9xz2hvT9k8ygVpzl1HXjG4qw3YdqNTVCa/j+4ve48/Ah\nCq4FKywwHbMxc1ajYsSh3Wqj1yXo8OH+jhP+pHULP97ltUR2kGyRR2N7aR0qpv5kwwkq0ZDmSzzY\np7Jl+ydDt54dZxkqRq68snbmqHWl0dTGWe+28RWmElwsLCoxXyOf1j7TYOqP3nkPU06pz/aOodkN\nuNOJcTzkVLsRWG3TgE4rA8NO4cvtLk7YjfnhzgGmjKPfvfcp7rBxXntpHZtbpMJ77aUXcW6NNjtd\nDLG7S1LV0XjkXIMDX4NV19jYSnB+iwZR62MsDWmgO90A7XjxjTjPSrRaJANuJ2uuPlJWF9jfo4VJ\neQprzMMKjHSQXBCGCPglDKSF4Ac8m6XYfnQfALB9uI80oxfgpz98G7t3SXlw8cJFdJgvlqczFAUt\nCul0ghnDmkmi0GmxK3UQQ/Lk86SEJ589SXla6vrk59rVwrMAJBfQTMpHUAwDTeQFGC6iKhQgFUG1\noZ2gnHwfAKDrApaDi6qsXDAFPa8pdZpnJjDn/uiqfKKotniG2nxC2ka4hrSGqw0HFSDs0nNrb1xC\nZ4XmqS2mMCXd4+P9AQxj99PpCAW7+a+vb8KwgixLp1hbJVils6SQct2y2dRD1RQbrUt0ejQffX9e\nIFz5gbsXbe3cgd6LoMXiu5SuImg2o+12I+RsWZEWwCHzal4/v4qAXc/73RDn16k/a+0Yn/L7V+Q1\nHVYARKGHKGo4SgIa84LYjdv2W+9/hCM+qJzfaqOd0HOczAoMeDFvtQLEzDmylXAFcyPPR+A9A5Rp\nDURTXPxUbUwIAcPjZqQPwTypLCtcjTTfC6A58AmS/qn5Np9jcTd08zxQApoPP6UWiBgeCKK5WjBJ\nWkjYdiIvKszJkoIhfprKDYz4tGZOVXCQUqJolJ1i7qqv69rBD60whseb/yc/+xhb52idWto4h6Iu\n3XW6bQoiOq0url2m9/XT/AEOHlMg9ulHt3D7k4/obzttXLlCnBTf97G5QdCb1AVmDCNXeQ6fTYHD\nKEaru/i2s9SL0eY5tbK84TiFMowhI75mbwmtZpOMI0xnNMYf7xzj7g7NtYO9AVaO6Dsb7RY6zJtt\n+RbLfCAZZSGO2dl9CRMYnmu7+0MENY3inVvv448kBQVvfuMbuPHS5wAAQRhDMseHyBqLBxrQheNM\nWatx8zo9l1/8+hfxlVdoXby4ohDHtFld3VqBbGDhyiJkA9o6TVEwF7Oaady8QXy+9378AY6OaRy+\n/c9+H3/yr/4lAGBdhlhdoef1cFqi5BquwisBHgcJuDqPss6hp0wZseKZSvPN0hznzlMQdHx4gJTp\nEjBz5XQcxmjHjS1LBcF8x+2hweMRrQ1vfv0b2LxOiZRJNcXRiCgJ93bvY3zUOOVr5DnFE5UuEc39\nl1FmfPhJc0g2Zs6KElvrtK6vtXtoM20xOb+KC1//2sL3eAbznbWzdtbO2lk7a2ftrP0c7TPNTO2O\njtHm9HBdaKyuc12x0ANYrXThwiWMDwkGGO+e4PUvsRdEneMqG2xmBfAHf/jHAIBer40f3SKjr7t3\nHmJ9nb7z27/923jjixSZP//8RTy4/wkA4PBgD5oVEul0jJKNPetyjLv3OHMkNF6+Rv3xwtCVh1mk\ndTtL2DpHqdkrl15CnXPdsnSCyZgi5zRNETHE4tlqDkFZiziZp1djTePweOc+bu0x7GgCHLCf1P7e\nriP5zqYjl3YtygwZw0hlkaHbpd9qtQNI1WRr7BOqmkVVRHVVufIqp6EzVrwx4gAAIABJREFUAcxP\n18bAcJakFj6aKmG9eh8d1WQEApR2i29bQtR0yvT9Swh9OvVqc4CqyTTVtSvvYWGgGVazxszJ5fpU\nORldnSKdWzxL4k2q+cneqgSvvELmslk2w6fbROr81je+goDrLg6nU0gevqwyCAPqw95kipIzUzeu\nKZc5yKYFwJ5KaZrBiqb0iIdGmCBMiYTxB6UCjFmNE8XK1RkUUsJqzuZECtfXFydLziYauWqgJYtA\nUn+SKADYwNWPIrQaMnIrxPVNynxmoxEmY4ZDagvNJ/IszREzRGWlQso1BGe5RsrOeT/+4D6uXKZ3\na20lQsHGiNmsRsV1/QoIbHBtOw8KIc/TpDMve7NIsxbOd0xrjZgJ/Z7yHMQm0ELI2Z3RyRHSKZ1o\nV7t9FGVTzb52cxtCuIxeEracGECXBUzdlARREJyZElJC67nJbQOnB74Pz29KWxTw/cbA89RvPaUt\nLy+5zNTJyQlaLIiIkrnxp7XWKXWFELh5lbJIH9+9h48/IjX15eefwzGX0lpdXcKMoa7pMMXDR5SN\nevedHyLnsVGwuHaBMhqryz2E7OdX1hoZk5JDT0LznJqkM3S4xIs+VatwkZYEGhc3KVO2tHEeK0uN\nKlTBNrVFPQ9JU48v8PDBPt37H757HxmbtloTYveY5uPt2QwtFuJ0VIU1poa09zKMWdzz9dcuYsQZ\nqAeHUxRjLqvzsMTbh4QGPN69i1//678FAPjiV34RgrNRAgLCLr7gGF24tUqgxn/5d34DAPBLX38d\nXkl7UoAZEFBWbulLX8XaGlFSxunIrQ1J0ELNc3B0nCHLCBZ8sPsY/+j/+EcAgLsffITn2pSFWW33\n8WGjlL14E12GaQb3P3SEeOlHqPlevKpwKIPxE1cjdpEWJh1MmbJz98E2Pr5Fa3wcJLh2+QoAQIX+\nnHpgJQybX88qg5r3jbuDIe7/kFSHj092UetGgFPC1NT/lhVY5qzoqvDQYzXr7XyGtMkASoUvvESG\nqp/eu4fLF6kPly5dxM1L9PkL/9GvYOmF5xe+x880mPrWL7yEmm8yMx4C5oq0uisYsYT18OgQb7/9\nLgDg4GjgiopGoYflpQ5/Z4j3fkLfWV5ZRkNq0bbGw4cE5/3O7/x3uHCB0ornz513fKjVfhudJZqU\n7fOXoWvGcaVFyfWdsnQCaehBFlmKBdc2AIQGDE4okNnfP8T6EnHB+ktL6HYpjTqbpcgy2gTbscLm\nOdqkdnZ2MOJ6dVVVoV/Riz0+vI9JyjwNdHDIjrEba6tQrEKYjYcoUwq+ZtMRNPd/qdtCq0sLivIE\nLE9QYzWMaWwK4MbwaU3X2gUs1tq5wzfgauRZSIpIQBJ5xVyYMKixIWlxriuFPXDtrlpCFrRoVH4F\nxVwFUU9gWalS5/Ni2AbG1W47rSgUmNcNpECvuadnIGkAqCvrOA/TrMLOLvNMUGPCPITf/+PvocvG\nfGsdDy9cofnleRNkHKBPZznOsbTcD30ccqq93ek49V9ZlM59PAwVPB7DJAkRNhu+NKhZHSQlnO1F\nf6mHlFVJQTVDmCweaKTZGAnXbCwrgVrxJhh46Ldpk5qmM/C5A2mm8TErYSqd4YShvbKuXNFmU2kH\nbmSzCnceECTw6PDIvUP9nofLFymYCgKDkmXa5ze72FxSPCYV4oaPoS1C/hxHIdJnUIIlSewgOWO0\nMwVVyodiuwJhBQKGvuVkiCM2Z0y6y85sszQVDMNgRhtXEUHrGl7DE/A8Z9ExK1Johl6iwEfgAufU\nqc4833fqQv8UdOv74cLKU98P3CFIynkFgqoqEfGciqLI8cbqusTqCt3T2vqrOGa+6K1333bFcldW\n+vjko09pnBA5bku/20LB6uurFy9C8LoT+RLpjOZ7WRYIWBnXiTtQMTuXW8CWzOEsCpTPYITYShTa\nHdq0jayQdLmunLKQDL+HkY9QUiC5czzBd3/8MQDg0SBFi81L290WFO89Y1Fin9cEWdf4HNduu3K1\njw927wMAtrMcvWW2NdEWmnmBmABLHIzsfXwbb/3zPwIA3Lz6InoX6CBvLebqvEWCf0uHPwBYX4vx\nwmXiRN7c6MEPaf+T0dzCQ1o65ANAK5vi/kPiB/3FO+8hZfhdpxXefocqeny0dwezbfrOK71lWF5X\nPoJA9yYZZtaqg+0HFOCIYgYZ0xpce10oPtRJrw3LHCIL4BniRfyfv/d/4ac/JSf1y5cv4U/++NsA\ngDe/8AZevE6Kd6kCZwZrpQfBa2HoAT5jdXfv/hhoDv6eRML7TF9IrLJ9wprnYauxXPEUmjRBLmK0\nr1BM8Oj+Li6sNobal7FzTGvV1quv4pt/9+8AAOKk2zgqLdTOYL6zdtbO2lk7a2ftrJ21n6N9ppmp\nK1vLMIbJuaXErdvkd/GDd2/jHkfXz13dwEqXos0LW9fx8CGdFKGFM7nb3GghyyhC3t3ZmXsaGeNO\ne3fu3cGde0RM9z2JLleGTwIPz12miPTqxS30GHZUfoRl9r7wwgCTCUX4sigB0wBVT29ZmmMwpBO8\nzL6PaxcpG9HqdtDrMrGzl0A1KgE9VwpFcdul5OPIAEwE9pYvYiVk6OjRIQx7tjz30vPIMjrxfTI8\nwDGnez1PotOjE2ir7SHgn5LSoJFmWChX/kDX+gm/qP9Q01qfqrtn5z5Q1s6NOiXcd4TRaBIm0gsA\nQX1viX1Iu81DUKOYElRbehJx49+EuTrM8yU0q5XqvDx1wvcc1EJmoXPPKaPnGbRnaemsQptVj1Vd\nY8gkx1YSO3+rb//Z93HjBhEh/7Pf/KsIojmZ/+iATQ/jEOfP0Snz4c6R88BaimJkTBRNksSZsJZV\njg7DvJ4yTqgVhyFyFmtYa6A1ZYLKvEDCRrP3jjT+xS2aC39/gXsUskTKpVxCr43Q47ngW6QVixdm\nNaYsjKqMcETdO9uPsX9E78fjkxSHDF/3WyE6bPI5mhU4GlKm1FQWCcNbL15bx8oSTcgsT1Fl1H8j\n4UrOBJ5Al0UiRZY7lV83jCHF4kdFY5w2AUr6rNoBhPAQBg38I5x56erGBaQpZWt29nawvELrRFlp\nRPx9UdWoNM2BiQQqLk1lrUXRyMVg0WpvcR80ioKe7/HJwGXKhJTz+pLGknkviLi/6Ls4GAyeEFY0\nfkwwFk1NI6Wko7kbU6PbpSyPrits9WM3Hh6TfSejAUYsWJkMp/jVb34LAJDECn/yh/+cfvfwAB1e\nT30JNwfbSjpINvAsspLWzdgXGPN8t0JCBYuLCIJAwjKGHndDeFxKSVrt5osXhchruubbH97G7pB+\nK+qswPB6N0lTLHP2TQkJzeTyaaGwtEFIxZXn1mHuUIZ520gUXL8vLSsoX3AfWuivU7Y5hEVhmlJg\nBkcD2qv2dh+ix/SLqy98+an3KIWH2tI79Fe++VVckLROPPrenyHmUj2tC1cw4Pm1s7uLI/aCOzw8\nwO//038CAPjwo4/mVAhfYsC1C1dqgec7NJdFq43xKmXWnnv1C4jbtD4NH5+gnNL7Paw7mLBqVtca\n4MxzWWhoy7QYa5yX2iLtj/7p7+KEayAur685mPLK1jLKnLJC02MPkyntYSq2iGLam4MygG2EWb5G\nr0v/vrK8hh7Xf9zIZ1gxDXSrEPHaL8scsxY9XxNJxCxgeHP9Imqm0P+t3/qbeLRD8ceLn3sZEZuR\n1lUF9Qx1eT/TYOrezs6cX1FJPDogaOfx8AjjlB58b/kyXmKFxGxSoWYYY2VzyxXmLPMjWE4ne54H\nwdh8FEinJkrTDMt9emBRHMDnVG4nArqcMlRqhPGUYbVa4uCI4JxxWmDGJp9Xt5bgiXkhyqe1oiiR\n8aS5PXgPQy72urZ+Hl0u0rm5sYVVXqhDrw3DCcIo6SFoHMLrAttsUPgoa+GAa+od7TxC5DUKJYuc\nX7C8zOAxV6fdT6DYsbkWJZRpLBM8x82wFq4QsJLq2dRuDbRn54GKPU2EsBYWc57IlL+zPQFGrKKR\nQQnv/2HvzWIty9L0oG8NezrznYcYMnKuyOyasqamh3JZ9IQsg4xAPFgWL0iGh5Z45pl3JCR4QCAQ\ngwVYbTVgG4NNW93V7XZ3TdlVWZWVWZmRMd+487ln2OMaeFj/XnvfyKiOcztKKSz2J5XqxMl999lr\n7TX86/v+gSQ/pi2UdouD0hyMZB1dVSgpgk9KBkv+L6ZSKOsJnimfhkGpxv+Mc46WnYerSH1Jv8m0\nHEQSI+42TEehk3EnhE+Zsb65izh0z7nIf4qcajy+dfuWz8j+yb3H+Pzbjs4uSuXTBtiWTFnmOSRF\nllVaeRnDKCAmf5/5fO4zsqdp3rTXKkRXSDAXxAaqrtlYFRjS/VlfQJGxNuiP8OjIzY9hP8C1bXcY\nyId9nJw6Ob0sS5981RrtxxFj1tP0UjC8+bpbtHc3xiiWbu6qCogYtWVmoGlz1MygpFD6XsQg6xBm\nwyCxehu1tg1NL+BTrggWeL8/a+EzLY8mu0goa/dyeYb5qfOPC3pj9DbcBtoD81J8Wqa+vp7WBinJ\nu4IxhNxFYWljcUaHnOUy9RGO4AL1VK9MiYCMkLKqVi50XFYlIvJbmfSHyEhuC8MQhgz3Kk0xWRvX\nPwkuaNxJiYLG16jX95nf01TjpRsuIvrJwR3cv+vafePaNtb6NM8WFutjKnirKp9qptIVeD1+yxKM\n5rrkBuOxk4rywiJXqztNKaV9tuxlWfrqCFoDS0p9clEZfHjXuUT86QeHSGk/AJNQJEervPAReXEg\nEEbkGmBDSKoB9+jgHI8olQ2Ol0iNm8c8iNAb0j37ERT54PRHI+y+5LLIPzo5xB//Axcld3L8AK+Q\n68ZvrmBMtYvE//CHf4H/Ib/r2jg/xunCre8LHuPJhZsTDx8+xJQOKhYMGR20gjCEINmrMAUG1G+b\ncoAFrZ23vvQlfPObv+GuHwzx4U8/AgDM0jNoigBf5iWWlNKFWeVdLSyXPjqdM3GliMUvv/15PDh2\nxuarOwO8PnZr4Ya8wB/90e8DAP7oew9x5wMn0Q7jBIIS/XKTIaZo4/1rW/jSV94BAPyNv/m3cPBd\n5z91/qd/AnnPGURBnABbTh5Nbuzj7S87KXNxeoyH1K6/82/+u5iVrj+/8vWv41uUcgO2SXciZVMc\nfRV0Ml+HDh06dOjQocML4DNlpjLBoOg0VLECN19xJ6Zbb+zg9NRJbGEAFNadOM6WMyyIeekXOVTl\nbL+oJ1xNAACvvLKLiHQszirs7DoLs6o0sgVVoS8NpHT36QUKhpzO88r4qC3OOLhx3wfVEvtUOZ2Z\n5ZVMTmOsT32fpXPcP3VOfdPTEwxI5jt+9BDrY3fim2zuYUjp7sMw9JT8fHqBOyR93j94BEV5My7O\njhBTZENVViA/c/SiBMMxMU2JhDJ13T0BQwkotTSQAZ2kjWkShHKxsrTALHwuHmubmndtZsuikf8M\nDFJy/EwLjSn9QZIIRLFjI4vlAiWV7mDhCAOiXy0sMlVXGi+gqCyH1gq2ZjG0hq7r6Bn9FFPWes6r\ngBko405deWVQkKxaqtInXbRg3imZG4Vrn/saAODk7AJrlB9qYxjjB+85BufatX1M1hwzeT4rEVIC\n17KqoKhcTRQEyOr8TcYAlIRTBszTgf3R0F9vDVAScxuH0pcnWQWTUYiLC9cuLUpkNMizE4Mv33bM\nxOsvXcdHlKwwijROqR5bKDk+/5abry/d3EJKUW9SFAh7VPF+YZCm7vvN7RFuveSo80dHpzimUhVB\nGGCbGNrz85mXqXPNoYhSur7bx4jkGR6kV0qgy7jwdfEsrGc4LANykmuFaEopqVbi2YhxxMT69La3\ncU5rzCdFgSUFA8Smj33Kg2f10peeCLnAkt7jaDxEHQzMuUFM+Za0lbDkxN3rN0zZVcJOgyCAJKZA\nco4RPZcqK+QUTMNCg5KCXUTAUai6nmSMikrhnC+mSMjx1yiFnHKm6TLDD991Neke3xtBU+LgjbUR\nSromyzKMJsRYVsqXdqqqClXurgmDAHFSOy5bvwesgiLTmC+p3qadoqItSyP0jH4VDfDeQ/dsD5YB\nMpJh9eIcAavragLnC/d9bxCDxUvqQ4MpVUX5048ucJpSXdAih1VuvBfaIKdIjOqCYZKTU/t6gIRK\n1Lz759/Bd/8v54y+udvHLFw9CxMXGoF19/ngo0Mokr1YdoyTI7cHLAqFqqgjNAGfo4w55hcAmCpQ\nr3QJgG0K3nrjC1/GL33r1wEAX/m1X8Ps0PXVD777I9x/98eur6oMnCIBKxU28pYNYeuke4LVJC4M\nVq/nCgDKcHz5i05xmh0/wMUjx0B9dHGMO4/d82QPK4xIEaiUAKfI0ERl2ArI/SE3WFAt0JdffRMx\nMfxhmoHvukjVnTfexPitzwEANm6/jvV9ijw9PML5hXuPX3rrbRhynREIIFnt/tLKl4hW2dwV8Jka\nU8fnKaqqrolkUJCcIMMAksJrF+kSC5LYCi1wTr5L8+wBcuUGx7W9ETbWSMKLhmCsLtiaY04TJun1\ncHLqNus7dx7hV37lCwCAMNIA6esoU3AKLQ848wkz17YmrYzgV0v4yC2HpTBwKAbQhnv0+D4Oj9xC\nepQMkQROVolHE2yShh3Hif+t+XSBc4owyIvUGxuL+RyiTp9glK8/OB4m6Pfd7xYCqMqWD4ZfzeGz\ngitV+dpcAQ+vpH/73acl87XhkmS6z4IJn3hTGwNFFKpSxmvxRbHwi3AfAa5R5mkFgSdzalNZNnJC\nWV6OKHzG63n6+6v4TRVLjqDn/jgvNZaUJDUQDAa14WYQUz27ydoEo5Ebjzeuv4rHhVuEz8+PcUHJ\n5j53+2UYmm7Glt5HxSUjrSPOAE7jzhYaF+eOhp6sT1D5dAjCT3AuAE5ybhgHmK0e6AZTttMhRMhT\nkgtVjPW+25SnaYE5+b2AWZzPaW4FHHFYy3kaW1TEeNjvI5B1eo4Un3/H+ZRdv76Bo3vOj9BajrKi\nFAjaYvHQyTNaG8yzpo21PProSYoLGtfH5xlu7q2v3EYhw1bKD+aNKaW1n99CCl85oKxKVCR1mDKF\nJqP4+4+P8H1ahKcygho4w2AzjPBVivj7QphgTMV2UeV+zBsDDMgH0DKDnOZlljMwMtDyPAVjdWFC\ns/J6szaeoKQ5scxSv7FnRYkRbaRhKFFStnpVZKjzgRqtEFIy1KJYYmfs1iAjAqRPXB+oPIPtkfy3\nWCCg8K2s0LC2rh8nUNXBVUEMUD21ZVZAkqwaRCFKqgmo9eoHN8BVvgjJNUFEAxj63RIM6yS9ysEG\n7pOfVC56TVRxWQB0AM8XOSyl/JhXOcKI/PD6wHnpDPqTeen921KrwTI3//K0QH2ijiqB/NDtK/eO\nz3D2gPxUEcCcu3kvxhzJFfYMYzJfTFgGa7h77O7DsxLckK8eAzjtkb0kQkFRlrFW4HXmewaEtDd8\n4+u/jG/9hpPz3v7q1zAaur76znf/JX7/f/s9AMDPfnIPN2660P83X3kN8cjNrcPz96HreptcemPK\nCnc4BlzSztpXeRW89+EHeDh39//kzk9gLty8vxmEWNPu/b69vY77pWvXTClskDvOkA0wNW5s8/3r\noDKDOE8z7Lzhnn/3lZsIyP1hvLuPgGrugnPvj/hSf4yXyWebw6DktYvE5Xd1lf2+jU7m69ChQ4cO\nHTp0eAF8pswUl2uQrHYQZhB0+qyUQkGOf1YHvjaV6EkEgXNa29rawgaVYBFg2Npwkok13EeRKcQA\nq+tgxYji+jRkcUFcrh0GmAzdqS3qr0Pl7hRQ5oVPXzGMB0h81W+LslydsmXaJbUDqAI8MRCWSaQk\nz5ycPkFgyWl+doH5uXO+DoLAy2a6tCiIucmLpafVVa6QEtf66OEnkKFr/GQtatGu2udJYmCUR8o5\nZftEmxpgoiXNmdWZm2fhaebnkuzXqt9Xw1iDgk7VRmtwytfDEWBUm/jC+rIJSptLcl27VMwvGspq\nJHTqqsA912uM9TQwAzCeuDGyyCqUFFV5cf4QGbGF7/7oY7z2qnNE3tzewZMj957DKERJ0qdlDIwi\nxYpF5pMwKl2hoIR6AiMwYg611r48CQNgiVWxykKVqyVeBYAkjLFJUjaHha0l1MJ6duzBWY6cJNQo\nkpjSybs/mXi6vFIKo7iWUiQEyXDjJMbeW7cAADv7e/iv/8TlvLm1v+5dyJdFhTnJ+Jsba3h920XA\nRQI4O3Mn1KPzDCE9z2K5xN17KzcRQgaejXLOpO57Kbj/3ljHmAKAyjKENCdMkOCjpZt/357mOKWn\nluMhNHOn5JlhKGPHTK1tjbBGa9v06ABLktas0QBJbrDKJ9nlDJ4lAuPe0VXIYGVZmmnjE+8GYYiU\novkMa+r0lVnuaxtKIXxy2SgMnXwMwFqJNHXjt0otBjQeY9HzUZhMKRT1PavUJ0MNw54vBWYZ8/nf\nFosFehTUEIRhK/o2QrliuRwAEBFDtnB9OYyZVyEqbj0zbJDgnGKENKsQEEdQaubfp+YWdTRCVWjI\nOq/XMGoCN9QUwlA5FgNfFzRiHJZqioZhDwHNRclLpIcuMWYUJIipP7kyiK/A2hiTQlCQhdaRT+Jb\nFQZ5HQxiGneFeJTglU0nm78SJOhRDsXhl97G5776ywCAX7r9BRweuwCv//G/+3v46U9c7rCP73zs\nSwft793EBpXEqrjE2QXtMUUGgKRSxmBMKxkpMa6MaQCrjVMAyLMF7j9xbXw8V4iXbvxMeIicWPc3\nNyy+QXt/FnOM6zxsSPBnVKJrHkbYecnJeaOdbWxvkU0gGTirJX34+c0Yqz0zwC0al3nOfH1czrjb\nJ6mNf1V8psZUwPpgFOVllIEwjtpk1iXmAgDDjQ8hN6rEm6+6aIkoiiBrv4557l+q1rqVmtF6ucoU\nOSbki/TVL30RgqhfZoGCil8qEwDC0Z+8D+9/oGWAJafkcGEI0VudwOMKkHWtoSQGjRMwbaDr7ONG\nQ9NCvbxQWFL2YcB6rVowCVPXX7IVBEXGxKGAFJQwsZhhOHQLVig1sjo6C012c620L35kGfeRS9wI\ncNm0a9VCwLaVDqFd3PhSbb5L/krNNbDWD1WtNRRNaqY1LPm2CFNBUh8E3ECaOn1D1dSvgn3m835a\nymv+fRWjK4oZpZEASmMhva4WgNE94zj0iWDLNMPBA5dh/6MPP8S9e26B3b++g+09d82jxweIErcg\nl6UCr9N5WEAXbryHMkBW1ckwVR3djjxdYmvHSRFFUSEr6sSrFtzW0WQKV3BFwbWNMTT5o0kuEFGK\nkCytIOuafQJ4ZddR8xeLFHLLzddeFPoM/qcXM8QJ+TEZ630QtzaGyOn9hlZj2KcDkgwQ96m2nWVY\nJ4p/a2OAbUqZMOlF0PvuwHOSG5+0U2mNJ0erpymJox4y8oPUyiCs/a2YW0AB5zNVUT9EvQSWjLvD\ndIl3l07OCdkEX6IKDUUSQVCy07e2tvAOJW4dMw1F7gkGErOZ8wNZmGMEdRHsOIClaDfBA++zFIah\nl76UUisbU1YZH8lqYFHV89wCM8rqzYWFtnU2doMeGVZr60OcnZ1Rd4RgFNEW8hIZuQKEAkjoULbM\nUhTkH6aNRlW5+4/HY2S1314/hiL/uUobSNoYZRghpDVdG6BPKTZWQWE15nTo2oxHYIyiTlnk1zKt\nDUQdySwtFKXqEDZH3CeZMqt8mhUhLXjivh/tTbB90/nUHMxPEI8pu32Y4CXywSk+eYJjyt6NpMR4\n3x3kh6MxFuSGEgUR9m66BM39foTNrfHKbTSqgKZkyuCld094eXsbm+ub9Az3EVN7/9bf/vdxbdf5\nLKrZHGxC0cbDHu5QRNs//t//CX78/vsAgMePDr0RCi58ZHBeVLhPfrn84AxPDp1bSVlkvh6pNZU3\nRpyhUf/DNJ9XwMhqGGpYyhgG5GKyMAGqtTopKMeIjOIgjtCbUFWU2++gIFLin967g698zfmn7u3t\neYNXg0GQ+wAXvGU1Ge/nysB88mDDANHKWP+LQCfzdejQoUOHDh06vAA+U2YqROBPhBCAJWdFCNti\nDgSsrZ3u+thZdydUY00jRdnRJYdpf5KzuBRdVlutAWuqu1te+uSP2jIvdcVh6HO2WG2805oMAgRy\n9W4KIDGsIwE1Q0FMg7QWlmqPRVJAkDygC+ujxbTWCMihWDIJFhKFH8SI6SQVSoaE6O3BMIahfFsc\nzD+nrSoYkmc43QtwyRNrK1zy0Cc4NcxArOiAbox5Nht1iYEyaApwtdgi28RKVFXROAdrhZI+70QM\nEaf3AANQotaqyP29OIRn3trP8zSumKvT47QcwCg3Bve3Y0SUn6Zikf/dXhjg43vOiTJM3sfogWOj\nTk+WKOlv+8MxDum0J5j1pTnCKEHmEzwa9Cjxo9IGU6qpGEQRcmKggiBEVeelEqFP5qnMEnNyFM3K\nHDkl4VwF4yhxehoAYQGaEsikxdGxe85ESAxiklIURxLWfDlDr+/eb5IEPkdVqZRnW8ajBJbyrbHF\nCW7f2vfX3Nh11Pw4jui9AkHEsU41JGMoz6Zu7gwRUIOnc4Vr26s7oMO4xK+Acz8PSM4xzPiErsa6\nyDYA0LAQVOIqDipcI3/yve3r+KWXHesg4hA9cgHY7McYEdt89PgA03PK/WMsBNWN05rDc+emSUIL\nzvy4raqqqZPJ2MqJArWusCTGhEcB6mO3DJvoQBHA/z7TCuOxY0zCSGBJeamECFHk7tkDBvRj1wfa\nVCBCH4PeAMXc/dZgMEBGJWrOzi4QxzWLn4BeP3q9AZZpTk1lSKh9ZyeniHq1C8XzUTKGwbZjZdf3\nbsJQsmOjJZRfx0usbbjvkwgwWS1zrEFQWwbLMSyxpkFk0aPowhv71/HW174IADhUU7Al5QUcjvGt\nX3cO3Jvv/ww//tjlY7KBwd41xxb1+2FT99Bo9IWTzOIgxi7ViF0JRvlSWZZlCIkx+evf+Bb+7b/5\n1wAAf/p7v4/79+8CcGzdf/8H/wwAcPfBQ0zP3Tp0NptjuXB9LgBPki7JAAAgAElEQVQEwrUxDBMw\n2uotb+pGijAGyLmccQZtKDhJl575t4z5fHHM8ib6vVW+aBV888vv4NGBy5W4ORc4pueMXnkNI8pr\nZs6eoE9O8K9/7Rt45a99EwCw8fob+DLZCr/+6AAv7d8C4PbUmu0ynENT8liXmlpTP2jPQDm3c+py\nMMh6f/gFeYp8psbUMIl91BaApk6cUS3pyPhIEQYOy0zzuV6TbGM1WWtdLTg4twN2iWyr6T0NwetF\njHut1Frra0yFMAjptyCsc2qAo8kFX10b7oUJ+hThky8z2Dp5prWITO0/xX04q+XW+5EFQeJrajHD\nwXnjrxKQYSUDjX6v9leIUFZ1EjUGQ3kShCmb8aG1l4sEFz6KiXMORW0vdeUnyfNw2XC5LPk1BZsN\nmH2WMdXIiUopH8VWVTkKilYc9SMEdY0zrXwIe1WW3nfGtiKeLvmB/YLwh9lrXs5DopCQYRpw4fsp\nCAJkZGT9xU8/8bJzUSrf5rt/fg9bY/IhGgQY9WjhkiEqeua410Oe1n4aGooSNuZ5iRktOMfBHKM1\nd6i49/AQy6UzxA6eHPlDQpYXsFcoAqyNREa+gJIzcPI1XKQVLihR4CgZodKNv2B9gFEwsCQDTPp9\n9EjOiQLrZSdrLeK6vN5yiRskd14sllgbOplnZ32CKdWHK3WJgIrScha60CEA/UCipANJnMS+5uMq\nyIvc+6C5A1S9mTbrkKp0kzWacQhqy2Qwwlcmrs93tzdxg2os9nsDH/3DAkCT20IUxQjpkCNi7rOt\na9lvfDOkRkX+U9pYQNQHG+6fsygKnzD2eegPe97fLisqpORbNOiPEA7qSDqOfO4MpURwZJQw9fT4\nIXKKsBuNYhjqj9k89dGHWmvyjXF2Wh1NO+wNENXRkL2enxMnh8eIScruJ0PM5xSZdb5ESZs2ZxxD\nMmRWgQlD3LjtEjxu37wGTZvqyWKKYc/12e5Lu/jlv/4NAMCHR/eh6JlFGEDRRrrOBQQ9c4zSb687\nG7sYUgb0a69eR1xSsWgZob/vDM83+69jGbm2aGEgaZwywSBqQkBrv2dYbmDl6msSN5VPQs2hsU2S\n7w++/c/w45+61BTFRYqHJ84YufjR97CoDwPeTHJyfUKEAGfc15AEmnXXcoGM/CPVbIGMXF4CyX3E\nOOfWExEMqikeryxYHYnJBa4ibP3O3/47uPO97wIA8kf38GcfutQIHxba+3r+6t/9XXzj6y7JabS+\nAU6GsAEwpjV1fbTVbCnW1lMIHF71hXuy+tla0mRL0BPAL8yIqtHJfB06dOjQoUOHDi+Az9YBXTJ/\nkmaMwVAODdgmgqXNe7Cn/q1bZRZqS7tNNf482tHlHWmiv/x1LYdoIQQ4UdGc8ybqhvMr5UXhlqGk\nqD3DtGcsuGH+NCcZIGpv4YBjQCesUEjvBG+N9aeMfr+PitirTGcgogS5raBlnSSv9P3DmPZRZzIO\nvOJmLGBq3h4arNZ2LIde9SDVkuqeLifjTz9Ggz1D5rPW+mesqgplSblhiiWEdaei9YiDU5RnVhbI\nKKLNGtM45Lci7Or7/iKhwj50LUEG1mkfcKeZWqWpGIOpHWCV8E6jjIeetVFc4GDq+uHwIkNInuyD\nJEdIiUn397YRkuwxOzlBVTaybUis5nS+wHffdclf7z8+xJgCK9Ks8uWE4jACk6uP00IrLMrauT/H\nJiWUDQOOGUUmZnmOOmLHCOb7PwpDf0ItoH0uNVggqEt5WA4p6jBbjgHV7GOMYTJ0J86qLBDQGBdS\nwhAjmQz6kPR9bhqWsxeGWFLgxiqQQnqmLwgCKF0Hv3Avz2jrIswAQEoBRu9aQYCTzCqhoClqMs0a\n5kjawDv2hmEfYeiYoaLMIepafgK+dqTSORgoalVbHxRjrIGmNjLBwYPVGMY0XaKg55rPU0R0ku8l\nkf/9i/k51upSGVmOs7ML6o8QEeWl2tvbR0qyXbrIcDGjgoyMeed8bTT6Q+d4bbT2QUKqUr7kk7ba\nB+tkiwUSSlBalqVnsJM48ZLWKsitxXjfycLoa5wuHDtTqDkMHFs0GcW4cdvlNDuQKXLt5kQgpZ+j\nDAEk9WsA7d0QClPi3sFdAEDYEwjraFoDvPuBS2jZiwWiHuUH08wzukZbBFE9vixKGvtKVTifXazc\nRq1zmDpRsSqg6fPDJwqPH9fscQ+MSgEJyzGso1EZABqDDICq1R5Yz84wsMZvnHNf8ieOYyRJPRcz\nVBR4YK0GY7W0p+H3Ts5a37vo0FXxhV/5VaxtOAkv1CXepATA/9X/9L9gm2Tcr/7GbyEZDPzf+Ijt\n1j5t0LgEtdUUBjxV3IY99f/P+tcvFp+pMSWlROMawJrN1z47OqsNa603atr+OW1Dx5hG/mkbTcYo\n1JtC2+ASokmAyDhv/BYACKKlhbiaNmxg/abDBPdZeqWQ0LToCGYRkpEVBIEfBBJNUVLOOPq0ocSB\nREkXmUr77Oa6KmFJ+FW28gZpEIgmgo8b1ASkMcYXWuVcwOdbt3blKFejLss+VR1tZ3Tjg6UUuG76\nu35XSpU+gq8s82byKgVJvieSucUIAKa5Rl41iRy1Jb8Ca1s1k1rFlj+FFr17hVlkuURFbeGC+eR0\n1jWIbtlkAGai2ZwpRoz+1sLyetOWWNDGNJ+WCMnQWKqTRlI0BhUllRv0B3hCSWeXeYHjEyfVaGOQ\nk9zCRYCqTviJAn25unwSBiFkHf2pORpVViEJ6kjZAkWd/FVGDXHOGSTND8MMMjIAQyn9JmWN8TX7\n0qpESXJhmhfoUf3MRHBImgeVsRiSvJREMeoQSqsDt/nBSeX6CmH1pdaIosaIC+vNXammEDDjfq4H\ngURFcyuOe8iUMzAuFin6JEfGUQJDhgoPLaKw9sNiTTQwEz6tiQVDURtuqvK/JWXoIzHdtbXkJ319\ntedhNpt5/xfGmoOqNQrLRZ3FvIKhSLeIx6jqtC3a+tQFDx48Rkn13cADLJfOSBmPx16OriqDIa21\neZ772nC9Xg8jMu4tAyKSSRFGfq5YpRt/VwbkxerZZa0QmBWu7xdPfgZBIfslSjw++MS1CwImrhNL\nBpAkpapZhoR84GxVorZRVcC9EW+0xtER+T7GAQKKKi4Nx/mF64dsqcHq+W0DaIpeVMrA0HiUwwAl\nSdPFfImIre6/qFG0Ur1wnNYR4BCIRWNYW9OsZ366GuvTAYExL0FbcO8u49YmWkdNAENrasUyMFq/\nyzKDNfWhqGVAWekj4OjG9AS2OVmuABmFuHbbZSW3HFij/eE/3tpDTMlu+/2+r0vJ2j5Z7LLR9P9V\ndDJfhw4dOnTo0KHDC+CzTdrJeFMWo8VYAA3D1GastNaeLXr62tpqbTNWQohnJnPkLWe5tsOyNcY7\nZEsp/X2ctNf8lrxCNF+pKyhKyGiMganze0jTlNKx8BE7gbDeUVAw5stD9AcDDClqKOISKbE4pRH+\n2dKy9A6/SRxB1zIGa5LVZUXhpTUpRIsaZZC1g36pIdhqbWxT9IxJz6popRoZVrei+VqskaoKlJTQ\nsqpyaFNHsAALOqV/99FD9Cm3zXlRIK0lMwsEfgw0UYlORv10dGF7fLmhsPqZJl1mCCkQwHLuS32w\nlvBsLcCIPTG2cczUxjSJPZmtg2UAzcBrKp8FUFTaYlYaWDpJV0XpWYlFmaKiyCXNBYYT91vL+Qx1\nQqlKlb6buWWQKwYRAK7q+6hPea9CiQWVzKm0RVHV516Bos6FxIw/IeaqgqBTuJQCjNpSlBagd5pE\ngXf4FSKAXtZySBMBpY2u88lCgWFJ8kllLCSxP8Zwn7SRCY4rKAsIwtAnIDVae0YkDEIf4WbQrBWV\nMj4iTpWmqXVoOfKMmCbTlh/gZY+qyv2pWnDhOeaiLH0JIgbuGT0uOJhpvvew8KVXnoetrS2cz0ha\nLFLn1A5gPIiR1AwY58gpkKEsNcKee+e5KjGfO/ZKSomcIi/DpNck/NQWITE4ZZbj9NRFuq2tTTCh\ngAhYi4TkRaUqyJZSkFK0oCorvwwsl8srMf1xGCGhhKlKK4Tk/C008+G6DAyRcdcEPEJJa5mNmxxD\nkNY7+XMwP36DIIChTKaSC4wjJzPpwDS54JgFp/fc00BflHXToYlxjUPpx75KeoiD1fNMAaIJqGEG\nTFAULNqJim2Lj2otq003gDHWigoVPiLd2qYGLWMahmoXFlmBMm8Y/jqpKXODnG7PGtWidX8G+Pqo\nq4ITy6aZJ55x+/btS9ewlh3wrxo+W2NKCIh6IdX6kkFUfxZCXDKsniXbPf3ZRxuYy1FejRQo/KDk\nbTnPWp9OQAjx1DPgU/dfqY0hh9W1rMXByDAosxxC19qzgOiTNg/tCw4HMkRAKRA0Mz6SSlp4HyQB\n5hfNgInGMNTGJ/YUgqMgo4eL2uBw7W3GqPVSRyDYZee0vwSm5RtlWsaoAHyqA6MUyjrxqtHejFG6\ngNLkT2YtWC2HGQVQYsYH0znqv9DS+iLNzNqG5rYWPmYbq/hMXW1iDoZDHwHJOG8iWJjw2fAZZ02X\n2SZSzLZ+y1oXPQNQxmsfUGo8va4t9/exgvsUEYxZhFR8Now1qsJJMr24B019mGaZT7wZJz3v87cK\nqqpAUBsskkPTxqHRGBRaNykztCq9H44yxvsfccRNugutvb+YZBaGGhwI4SW/Yb8HS3+bKe2j4ZS1\nvrj0ZBCgItlJK/ff3D8MlvnqEpFSyq8lZVn6KDVjrZf/DOBl3CAImogsazCihJyMMVQUVcqt9QWu\nSxmgKklGtMrPhSzL/BwJZOA/h0Hja2ZN46oQx7FPj+LusdpkjKIIy6WTqJJeDyPyaWonXeQibiKb\nTFPsmaGRNwUX3t9qOlv4Pk7zY4wnE2pTDkbRosvl0vdf2xdtcTHzz2aBxu+QMQRx7ZcmcXJyslL7\nAGBv/Rom8Trds32IYpcOzvV7NpcOM8xHKRrTHAZ0q3+5aEcDW7/uu6jyxqDgrVQsdbuCMPQGtLUa\nrHZDMBycrS65t5+fo5GLmLXedQKX8v40K5oFA7Of9u+1DN4AtK3odwYNSXuS83mqxzuDorXEMOsP\nacxagDVGXFTX6ePCVw5YFbVTjTCtBrDmXfyraEC10cl8HTp06NChQ4cOLwD2i46E6tChQ4cOHTp0\n+P8TOmaqQ4cOHTp06NDhBdAZUx06dOjQoUOHDi+Azpjq0KFDhw4dOnR4AXTGVIcOHTp06NChwwug\nM6Y6dOjQoUOHDh1eAJ0x1aFDhw4dOnTo8ALojKkOHTp06NChQ4cXQGdMdejQoUOHDh06vAA6Y6pD\nhw4dOnTo0OEF0BlTHTp06NChQ4cOL4DOmOrQoUOHDh06dHgBdMZUhw4dOnTo0KHDC6Azpjp06NCh\nQ4cOHV4AnTHVoUOHDh06dOjwAuiMqQ4dOnTo0KFDhxeA/Cx/7D/4j/4Ta4wGADDGoemzsRaMMfqe\n+esZY5f+zekzB4MUAgBgW3+rrAGecR/L3P+ehmAcHHRPzqGUoucxsNzZmRYW1hgAwH/zn/+nz7jL\nZfzu7/6HVvCY7h/CCHcfHkqEYQAAKIoSUtRt0SiKwj/zSy+9BADYXFvH+ekZAGBtfQ03b9x09xQx\npIjoesDauq8s0mxJ9y+wWCwAAFoprI+G7vsshZTuGYzRWJtsAgDiOPH99YVf/tJf2sb/8tt3bFG5\n51Ww4HDvAab1rhgDg/XPBfrcBmMMnPq4/TlgDJF09wmFhQzc/UUYgcvQXcOBgLt7JoFAKN01UgpI\n6YZ0EEhE7nIEHIjp2LAFPPcdHp+d2kvjp35mAJa5MSsgUMxcH//xP/+HeHL4EQAgXWQYrF8HAPzq\nb/0NbGztu+dMhhDURmMrWFu6ZzMG6dzdZ7ZI0eu7dxX3+zD1oGV1P7rx/iwwxsCoaVuTtee28fP/\n+r9l67OU4AEEd2N/MOhBigQAEAYxwF3flpWCVa7tVcUgAte5YVzAv3aE0JX7XGYXkMbdvyw5crj/\nUFqNvND0FBwycHMLVkFY9+6ECGCp7YJJPx4sL5BXOQDgB//3339uG8MwtM/qr/a60n7PbXDOsbW1\nBQB455138M477wAA1tfX/TVRFKEsS3pmgSBwc2uxWODg4ID6qsJ7770HAPjkk09wfHwMAEjTFIbW\nlfZzhGHoP89ms7+0jdf2xpYz9x6MYQgC13/Wan8N5xyq/h0pmzGotJ8Iy6yEEO5vlVbQ2v39sJ+A\n0bzRSvn1kTMOZqx/3nYfhJG7D+MMy9S9qyCM/ELFjPEz8N7Dw+e+w3/j7/5OvRTDWOUnY5UrDPsT\n95zJBCGtDSwwyJWbTzxU6PXdWvzxh5/g6PQJAGC8GWL/5gYAoOAKd+8dAQBm0xzDsVtbb93cxu7G\njnt+JsEDeghuUVXU9kRiELn7f+f/uYP3f/Che56NISrpxvsH/+j+c9v4j3/vH1mrFTWycn0EALCo\nqM9hrV/brNV+HWjvf3me+zGYJIl/j/V1z0L7+2fNCWvdXv3p7y0E7cG//e/9O89t4//559+2d+7e\nAQD84R/9AQ4fPwYACGNQFrTvGmA4HAEAMgOYrPT9kGm354wsh6Q1QLMKJQ0OxSJUlvZazhHRuiWV\nhaF+SFmJitYVBontHTcGxuM+jHZ9boSAprE9P5vi7HQGAPiLP/zD57axY6Y6dOjQoUOHDh1eAJ8p\nM8UY86f8IJDgmk5JLQv6Eixap97m5N02ERlj3rrmrWsu/S4XADFExhhosva1VTBorHFveVsGq23z\nfc2+rIDDg/uADeh5Qljh7qNQNSdR2zSCWSDNUvc82uDRg08AAP1eD8dH7hQrA4nbt28DADbWdxFH\n/bpTENPJqD/o4ejInYbLskSaZe6ntMYodtdHYYjd3V0AQBAEUJosfxY9gzv6OTDWs3mibgscu1i3\nz72Puv/Mp++BT7OO9SmHcUDkrj+4ySFix5LoZIIypBNYlUEodzpBP4Ki0xjnAoxOKkII9HvutBoF\nAkq677cG0XObaBn8+3m6XwyNNSkZMmICp+cnyOan7nsmcHj/ZwCA//a/+M+gjXuGjbV9fOELXwAA\n3Hp1H2Ho+uXBxx9genbu7jNP8eobnwMAfONXvwlN/aMth6kpgp/DpNif/5+eCRlwGE2MDzQUncyK\nIocJ3Hzk3ALMja/SGnBiPErFIYkF6QUNO6mUBRfEXogKRDxDiASSuWvyUsOYpnM1MYyc4dJpuz7n\nGWPhl4fWKfmvguexUYwxzzz1ej3EsWv73t4erl+/7r+vqspf3+/36TmNv55z7j/neY4wdGPg1q1b\nePfddwEAH330kWePjTG+7Uopz0A8F9Yx5wAguPAMOuPMMxRlWfr2VqpCSe952B9AV+5dxb0EvcHA\n36dGFEicn7pxrY0GF0T1Go0wCvwz+OdlDIVy94/jCL3EzV0GizR3zIJlDGzlxQZYlnNsbIypXQnO\nT6fu/kEPIa/nMsPpxQkAYG2zh8mGeycHx/fBQrfGXbu5iYo5lmFjZ4SAaOuL8xQ2d23mKoTJiSEP\nJfoDd83yJIUmVnaplsjd0oreIIEeut+yQYWC2JOgCmB+zrr3LHDOYW09rnlrB7NejdHGoFlTOYT4\n9Bi2rTW4zfYvl0vPHo7H42deU/99/f9Ps1P189Qwxjyf4m/h7/2D/xkJsdlFVvhWZmkOxmqViWG5\ncGt/PBygv+Hm4vHFDKp0cyUoC7w1IuUnSHBO+/TBssTUs04Mkrq/AFAvH0Jp8HpdYQyLqVN+pCgh\nyD64WOYQ0o3bMtdgdvX3+JkaU1nRSAK2qiDq12GeLc8B8BNPCH5pMaw33zAM/eJW09CA69D6Gg0L\nS4sIjPH3tNYtPO6zbVGezC/+Smt/n1WwmF4gTnr0ryUC7n43tJVf7IIgABNuMbIyQdSjhTfLYXO3\nQVsBDONaFixw/yNHIZ/1DxCGsW+vpEWz3+9hsbgAAKRZhnTp7pMkPUhadKQI8OjRAwDAaDSCJGp8\nMBj4xfeLv/yVv7R93DbT3cKiPdQub4Z1XzYLwuU1tDVZjYGla0wxQ3rs6OCb1zZ8H5xUKU4zNy1m\nJwcIlGtftLUGQxu71tqPBWstJHffR1GIMHTv8OWvfuEvbR/gxlwzDm2rJc1zCw48fuCkveX0GFy5\n749PzlDQZ2M0jo+cofT4g/dx8fCnAICf7U/AuVvcIhFgtObkJFMB1665TVsICU0bE2fsWUopPWw9\noWx71XsunBREm2xlUL9IwQBNRraxgOSuP2NbYEybpor7mJYFPXMMRv2sqwwabqdRJoUAbb5M+wVc\nVQrGNIcoRsaUDAQ4GUpuLtKCbwHTItDNs/T6vwLqTSSOY/R6br5KKfHqq68CcPNjOHSS6xtvvOHn\nB2PMG0dSSj/m8zz3608Yhv76MAwRRW7+bW1teSNLSukNq7IsLxlTq4Ix+HcuhEBF741Z+N8JwxCG\nZAtblFA0RGaLuXdluHbzFm7ffgsAoLVClrp3uLezg7MTZ0y9//77mE6dIWMUYGgTi+PIP7PWGn6P\n1xUmfbcpBdzixJAxxYXf2FdByXMEozUAgKosjCDJZjlHNXNjM4giTFNnTCHcwGjzmvs+5iiNWyd2\ndid4fezm1my5RFlb6FUAZOQaoAKsxU5miuQapic0BrMeMlqXL6oCy9S1ZVmUqLfqi3QGTW4cWaXA\nrzAXYVtym4V/p1orP2/KsvJrkhAMSjX3r/tzNpv5cdcem1mWeVeSMAwb8oE3e6p96jDcrH/N3HPf\nNeti2/B+Hu7e/wQJGeNVmqMqKmpjs4OEQez3pH4cYTh0RrSWIdYi98w35yfY1W58MgPskavK7ijC\njJ6nxzkiWtA+nk9xMXVt51ZiWbr3pbmBpb0iZkBJcyfPlrCG9pCCQVzhhNrJfB06dOjQoUOHDi+A\nz5SZciwFnT6NAfcOdfwSI9VmoGor+lnOmgCdhkTtgCyR57n/vj59WmthWkf7WgpiTzvfeemN+Wuu\ndMIAwMDRj127DEoMyXjfGq9D1dIhD7C5d8P9h3DkpYIoapxPpZTIMteW6XSKNHUnoyxNUVV0stMl\nFElNpU2hUkeFFvMFWO3czxVU4Cz8vCy9BZ6VORjqd8GwKmcrGIM/O9uV/+xT4LwRZK3g4PQOZw/v\nY7twjqJ7MsL8iTtxhr0bqErXT/fefw/F1Ema8dtvYDIkKVBrLxmXVYXl3LW11xshTkiWWIGZEi1i\n7RLzBgtBjOKTR4/wJ9/+AwBAMT1BP46oXRKcu2e4vr2NG7vOiVVyidnUnfJNeoK969sAAMZinJ47\nRvHzX/s13Lj1unt+zRtGxgLsGdQUu8RYMfw8J9NnQRsDo2o2xHppWhkGQy9YmBybA9eW3SDFiJzv\ngz7wKHefP54WEL0J9ZUCrxkI3cgSnLs5RU2B4I2cXo8gawFOgQRGG0+4BSLwrLJWxt1sRTzLuRZw\nDNGtW7fc/YPAS3ucc88i9Xo974De6/U8GyVEE+TAGPOsQBAEl9atmq2Josj/bRiGfk1SSuHhw4cA\ngEePHl1iCPTPc3v4VPsaeVxIDm1JLoFBUbjn0tZA0DiKgxAprR3XX76J6y+7Ptje2sGg72S+9977\nMYZD9xmc4ZVXXwMA9Icj/OiHPwIAHD1+BEuSltYKEUlmeZ4jJgd0mArpzLGy40GCzbG7Z1opBFeQ\nwFTRsM3LtECP2K7Hjx8j5o45lGqBrHBz6MHjOaZzJ+f1JyFee9sxTbk6987HlgkcHRPLtgjx2rZj\n5XaGNxAkFNxjLe79zDHPsg/MAtfeaBChl9C4FhwpsVQsNti44Z5HQCC4AmtjrYauHdB1hdoZvSgK\n/32e59C6XmM4OK/Zb+MZw4uLKZK4llbhx9FyucTJ6Qn14dKzV9baxvG6JTUD7T2Ye2md88Y1g3N+\nJcWmzJYAc31VZRWga3eJoMWyMWxsuDmnbIGjE/fMG1s7eHPbMa2jD89wfODkueFogpe2ndvKK3s3\nIUdunxulKdJDt4eYMscxrUM6EDhSjtXKrUVFe2SRpcgocoYz5nwOAChowK42F4HP2JiyFj46QQjh\nJQpT/0d3ld/kBRdeZKmqFs0puZfMTFl4upFb+O8tgII6yKIZEEK0NGljoYjSC4zBMnH3GRccpW7o\ndnsF3fS1116HVU73LdJzDBP3a6O1IVLtBkQ02kO07gaBqhQEGVNWSG80Gs4hyY9hLV7DiL6vihTZ\nwk2e0+PHWMzcgIskIANaCOIKjKLgLOOISUaK+yOIkF45Z4il+11pJQKiqJ8HDgtRR+ZYQNfmhrVe\nX7ZtaYwBnNUCmUUdHmSZ8D44CUrYpWsHT4/w8rZblMxiirMjN3HYrZvIMveuDp8c4vzBBwCAawOJ\ncuRkGiGazXC5XOLwifvbjY0dVLQIvxBaxsvPfvYx3v/wY/f8PMfmmntXk40+Fgs3YQ8PD5vNOQ4B\nMkOrSmE+d9cYZrF30xlQX/zK15r+MfaSoXo1k361ttReD6qC91uoVIVxLUtJjjXjDPQ1m2FDuGfW\nag4MnJFYxLs4qKeH5GCL+kkjMO42WS44YGneBww8oJaVtmVkMWjqHy4tEhrLMBIXmdscLSu9fHVV\nGGO8bPebv/mbeOONNwAA8/ncGzhZlnnjaHd395LfUS2TjEYjLwsaYy4d2Lxbgdb+UAfg0jX1M7z6\n6qs+cvfg4MBvZO0D5PMQBInvj+Uy9QeSt976PAryh5rNl5DkD6JViTHZor/z27+NyZozgvM0wyef\nOF9NU6TgPdf36XKGmA4J/V4PuzsuMnV+fgZDxpoxFUixgZDCG1Oq1AC5F6SVAVRJfQDvt7IKIhbB\nVs3Jxng/rxiWZHAZBhgEbv6FfQ7L3DXzeYk8d89wkS6gK/dsRQYkoVv7gt4W3tz+ortPJnF24cb7\n4ijD/KF7h/l4huGr7m8310dQxq0lShnkFHH22puv4HzDHWzPjqZYzpr3/zxYY8Bp7dRGQ+taAqu8\noXR8fIztbXcA6/V6OD8/p2u0N9zPTs8QhTVBobxv8PHJCWwBVbgAACAASURBVD55cB8AsLvc8+/d\nGANDfYiWzfC0z1Q76tobWZz7iPpVwFXVRJVaNNIz3FoHuCje2nBe5AtIekeDKET52K218uwAR8a1\nce3G67j++a8CAO4tclyckD/dgwc4euBcRb43P0Jp3NgYGA5LvrNWStBtMM9TqLr9TMDSOmSt+rn+\nlc9s48pXdujQoUOHDh06dPgUPlNmyhjd5IRSClnrNFZbv46ZIQtWNSc/40xY91lbcKKKmWEQFDEX\ncdGiHhsaTwbCR+eZlsMbsxY2cN9/LhmDDj34WGTg3tHVeit6FYzHI8zI4W16MUdZUhfHJcY7zgFy\n6+XP4WxKDuJRiDrtzsHDB54FW1tb889qrfXMURz0sLPvHDKjKMbHH7kTUKkUInLeG63tQBBtXzGO\n0Zo70QzGY5wTBa6MhqK+fXJwiP1r11Zqn4CBbFGf2v+/gUH9vMZHn2lwSPpewMKyJpoiIbYinB9A\nHbuTx57MMIkcXZv0E+y+5li1j+wAs8IxI9NlhpQk0MVygZhOG2EYeIbl4mKOiwuSGSYjlOXq7KIL\ntnyGrAZ42nc2v8AiJ1ZzkDgtAEBRWmxQf8/PH0JS/h5tSnBJTrtBgosL9/6XqsCv/raTHpPBGBmd\nFJkQLabP//rl53yKwbjSKYoDAeUcK8FgKvdb/SDHDYrUyhcZjudz9/0E2Jy4tvRDgWXhJMu9ySZK\niuZbsgBVRZGVWoDiPcEkQxiR1NRLUC87wlovozNYMGIUdJkjlPVplWNJr44LgV4/XLmNT6Nmo9oO\n5Ts7O5jN3JxI0xQ7O45x6/f7yCgitt/vX3IQf9ZJXUrp5b88zy8FY/BWhGnbMfytt5y89JOf/MQz\nDfwKMiZjzDOfs9nMn/zf+qW3fVtnszmmU3fvssya++sKZ4eHAICz8zMckywyGvS8E3k/CBDT9VXI\nsb3v+kajwumjuwAcs7dYOiY+DCQUsVRSCvR6bk5kWYbphZPh+r3eJZeN5+Ha9T0slo5xSCKJggIf\nuGjW8uUyhab1fXN3Czs3HHM0nRX46Y+dlLq1N0JRsx6LHFtDx7K9ee2rGGm3nn589ydgNE5nFws/\nd5NYYEDjTpUlpjRelssMBdFy1/cTZMRIHz85gWCrj1NjdKOoGOOZprIsPVNTVZVnR4UQ+P73v++u\ntxavveak2NPTU/QS97u9XuJZ1jt3PsZHd++65zcaguR0xphnprhtxl2bmTLGXJK1a3DOr/QekzBC\nnhHjA4aApO+yVD53X6EKHJ25MalUiiB0z3SAe9idOWZNVBoz6d6XmGxCbLr3+DB9iEdL1/89q/y8\nqMINLK1jktUshVG1+wuayH9lYDSpW2Hg1YGKVXhmgsqfg8/UmJJS+kZqrZuohVbYZzuq7uf5PPDW\nImatpb8HSsbI6gIk5xiEtbSjYUSzGNb7pDXA3prr6G/euoH7T9wG8cODc8Q+EglX0oYZFxAkUfCo\nh/VrLtlmb7KJcLxFzylwQT5QG3s7foHTB41AZhhD1HPPX4ehA4BAgIAiTq69NEZCiese3PkA5dJt\nfIP1bcihS0g2TQvkNIDUbIFZ6hY+EQSg3J+wgcDj48OV2sdtQ0kDrdQVzHpjyljjN0lprUtNQX1T\nG6l9lSHInQw3vfcBJmxBz2VxQuHYQm6iv+MMk+rcQpG/R7pYoirdIpNlOSwlJW3r+0VR+aSwYRhc\nyZ/oUgIOZ0H5z4bk3/PzM2zvOQPUqgIK7l3tTDYxJBnoq1+7Dm0ouq2coU/yydHhMfTStWV9Yx9r\nW3vuGssamc8+K8nHLw7MWN8sDo6Akoi+sRGjV7rNa76oUFDCzGlhcUHKxYYEEub6P108wnjiNk3F\nLDTJyzxoQvV5CPT7ru0Rk6gTTQrujHMAMEqBkWmepxqG3u9kEiNecxE7TOgrGYxtCCEwINn84uLC\n+0m1fZq2t7f9+tROUcAYw2g08p/rlAZhGHp/x3ZUcVvyC4LA30cpdUn+qw234XDojan6N1aBaW28\ngZT+YBPHEW5cc24E54lEALcJpxnzfjRlkfmoPV0VmJBPU1kUSCiCViiF8sIZDtFkjGTgDMGbr7wM\nQWvA2U/ea9Zioxv/F8t9fwghfJoEa63v41WwXM5w/MQl1RyN+ujTIZFz+OjF2SxHQYkfXw22YWgs\nn5wc+bHGIJFXbu2TPECk3Xvb6e3g+//iewCA7/z5txFTOpXecBuw5KfKj8HO3BrDxz2AZMd8niJO\n3POcHJ4gJqlxY30TR49PVm5jO12P1dq/I2PMpaj12rjPsgwXZJxyzv2YGgz6CGRjuNd/u7G+gTOS\nCzfX1ltJQZmfo0qbS/tuEzkonkp5A/+7V0FVaZ8Y1miL+hECGQBkyBVVDtTPzzSK1L3HRXmENeP6\nMzUJCho/k1iASzKaRlsIc3KjQIkJpdB47cYtnEXuvZg099Jwmec4oTREUmsfYcxF4zPFuITgq5tI\nnczXoUOHDh06dOjwAvjMHdBrqhIABMkM4I2DrbamcUa3tikrIYSXcBh7qvxMLR0a3YpOaBJ4BtIi\nImozYKLJk6QsXp84ynB9EOJn++70mTw58I9wlbwvANDr9xFT3qhoMMCQnL+tTJCV7qbzg0fIKPNb\nVhVQxBxZDiyJKhaLOdZqR0HV5MMqtYEm6WVzbYL9Gy4vThz38Of/8o8BAO99/0fojclZcTjBMCFr\nX3KkRJOLUOKCImBUpVdup7AapsVM6bqjjPYnHgHmJSpuDBQoegQCITn3iYtHWB46J8EwO8dkSENR\nl8joGYtKwWughkFTrhdVpJ4RK6vKJ9JsSypB0ORZaScuXAXc2iaSjjFYKoUimMGHH7qEnN/+o3+B\nrHT9Nx4GYNaxFZO+xPV91/fb25soC3eyN2rkS8IcHJ1iRpT3b/7WN7G+TQwX0JSNgfVJO6+S5HBV\nMMNQHw85s7g2cr+1PwRmx1QehMNHDSnLUdBczLX1QQWRSqEXjlWxYQBGjsaccUTEcMRJCEsJV0eC\n+2guZS0MsZY2DDyrGIUDWJorUSQgZH1SvFrEYhvtqKfBYOCdeZMkwdqaWwMWiwWWlJ+tLEuMx2N/\nTc0WtJlzl2yxeR4fWdc6zT99gvf5tlrlWeI4vvJJv77XnNYCrTXiASUQtRpPDpwscvrkEXid2NMC\nc5K++/0+BLEwcRx65/Xp9AJLYs2DOEQd2llkqWfnFAT0jZcBAGle4PDhXXc9M+DEj1Vl6fssSRIv\nq06nU19WaxVIKWCJ7ZIGyIlZT5LI5wJMZxqgPETDcQRV1WWPLMZr7reWyykCkq+LTGBr6Obc8mSK\nH737ZwCAi/mhd1hP0wpJ391n7XqC6Zmb36gMZEjqgbIoM8q3phnOqd8i2UMsVw+UMMb4vpK8YT7P\nz8+9LHx8fOzHb6/XwxkxmVUrR9lkPMKAAm3KVv+HYYh12uciKVHlTc6peg3Q2nom0RhzKeKvRnuM\nGmN8aaJVUBTKrxnGMBga+z0ReskPxkLWn63AgtjGHZ1hl97p+zKCjmltEJVXQ/omgRm4Nh6d3fFM\n30AMgKFjaeWa8XkZ59M5jp64pNhlvvTPxhnz6pCUHL3BeOU2fsYZ0HFpUapqOpMxnzKBceYjfNyG\nXG/c3EdFu0W39qloNk1tjE8QCc59NJ9hVNcJQCgCREGdGIzh9qT2YeCY3aXM21yCEyVZpdUl4+F5\n2NzcRETU/yLP8Kd/9gMAwP71WxivucXo9PCRT72QFjkUOWtZxpDXm1G6hGgnoyRnhCiK0O852jKp\nIoQUbbO5fwMvvflLAIA7D47wwY9+CMBl5B5TBNHWzhaGZKCxyiKlSfvw/gMM6JrngRsNfikltftk\ndFMvijHmpUBuuZdhJVMARe2d3vkL9CnB4/YwwqhHfjdZiZLaqiwQy1oeYDAlRcCpAqKWw8AuGYLt\nVBq1kZpl2ZU2K8VbqSKM8gngjg8e4P/4X/8+AOAvvvN9jCa0Gb20iY1N927L9AiPPnHPefQgQBzW\n/lwCSd9d00+G2L/mZNjP3X7bp1uo1OrJDF8UVgtY2viiSOK1XTcPAnsKQ0lhrcgQwEkIRkvkmpLu\n8QiWZLhQSmhanMuiACffK6st6rNSYANUlevQnk6xQXLnVDFcFHUSPQ6ack6iqGu8AX68GV2+kDFV\nj5N+v+8NnyRJLkXh1etTnufY2HDvaHd31//tbDbzvih5nnsjvZ3807bqqLUTySrVJGFsS4e1/Fhj\n1TYKIS5lY//6178OANja3sD03M0zlc/8QTKSCca02TJm0KP3cHqx9OvR9sY6jkkeF4HwtRkZsxh6\nHzsJve6M0TduCyQUnXl2+Ag5uRq0oxJPTk68PCelvJIPKgs0xhtuzer3EhS1TMoZJBn6y3nmN8N+\nEqCiGo9lbqDpXU2G65gr92zj3hDjxL3bT378IepkL0JyFCSJluk5hhP3zFs7E8C43+0PB2Ak/8Vx\ngnTuvq8K5g2uzU2J0drq0cOBDHwqm8eP7uP73/suACdH12Pj7OzMy3zGGAQ0vsIgwNmZc5dYLhcY\n9ps0MbWMzDn3BEUYNEk7q7Lya7PSzd7cTu3RTjEkpbycbuhKiUkZRF03VUqUlHjTlE29PGsFoOi3\noj4GQ/c8rxYa49Rdc96L0KP7xFWJKVWeqKYaS0YVKbIptmP3/Gp5hrmus/ILMNr7g6CPIHH79DJd\nIKkrN1juUzbBWozX11ZuYifzdejQoUOHDh06vAA+U2aqKEqfm0K4TH7uPzALOjyBMeYjM4xpSsIw\nMM808Bbdz7hoSAStveTXpuPzSiMjx+EkMMiJPrz5yk3sECNTFCUefvIIALC0GqFoGDRc4SS1u70L\nEKPEl6GXGh/cu4sid460o0ECRaeAg4cP/YkjiiMvlc1nM1Stsgv1ibY/6CEhNmoxnyGnSJpABhiN\nnRX99a9/HYuzf+LaMptjdu5Or9PzM8R9d3qOewmGtVOtsbDVinKm0qhDdizgGRze6m/GGpnPMAtD\nEXCiWODxB99x1188wu41KqOCCkuKiokGPaSFu2mWF0hM7Q7OYUvXVqtKn/8mSXqXghFqZ8zFYuEj\nI8uyXL3eGRxDyIiNCgTDoztOjvyn//D3saAT/2u3XsLd+y6p35MHGlt0IkzGfUiSBcdxiI11RxOn\neYlP7rnx9cHdMySTfWqjwqDt4PlXdLD2f78iNtbHKInp64UMawP3DNm0wgX1f2kqhHWRKwbMiUUa\nKg5p3Lwsc41cUY25IEG1dP0fBRKhaOqNzenUvhFnsMpJnyIcAiXdRwGgqJ44Eogo55s1BYrczQMp\n8VfWPK21Xj6Zz+eedXLrDbFjLXZ1MBhgc9PN1yiK/On/e9/7nj+137x5E3t7e/76hJyswzD07gzn\n5+f+d+vfA1x0Vv1b+/v7+PhjF806m81WlqSjMPLj/Y3X38Cv/GvfAACcHB8AVLtyczLxrAeY9HKe\nUQo9YtD74zWcnLr2pWkKQ9LeIlPuvcCVoipJes2VRUVsai8S+MpXXK6fj36a4MOfupJJhhUoK5KT\nBIMm6d4CXlZdBevrQzBKKHtxeI41kjJ3rm1iOney12Tcx9FjJ3sxFSOkZ5PlOl7fcVGNr157E49m\ndwEAVWbBcsdQnJ6c+ESR8/+XvTd7kuw678R+5+43b+5VWXvvGxpAAxABkhKXEUnRGsdQY2kk7zN0\n2P4D/ODwsx/84r/AEWPZEX5wzHhkWROekChzJErUwgUECQEEiG400Oiuqq69Kvfl7uf44Xz3yyxw\n6ayhjPBDfk/Z1Zl3Pev3/ZZkgioJb/b3BsygHo76GJB9ValSQplKb64zwTAlEchRBt/Wf681ApQb\n87P5LNuGQePNwcE+nu7q60Se4+zwkL4lIIlmORhP0CzaL8Bjd5Kk6BEo3/N9FqoejUawSX8qTeOZ\n7FI+w8gzuV3PlvlmyQJpmp4rX1+EmKWUwfqRhmHwvJGKfMoCFyYktZNUCrjkq7iUT/i3+5mEHVO2\nP8zQO9Ag8uQsAkwas89O0XL0e0n6j3HSJ+2tSgNuU/fXpXoDz13V7Pp9JMgjnbWEIdi3MVhu4vqt\n23Pf4ye6mCoHLlPac2ki8CjlZhnn0uKSaNFZPqMGLKbKyQLgxUieJlOqMjBjYqwpp4CeHIvU3TjN\nEVDDsqpLSNb1A01Pj5GRaWXc72m2Ey6eznQtBylN4p5tY9gnxtpghKe7+sXfvHUTrWXdgd996+8w\nJKrttevXOfU/6PTRnZFxKBpuVq/DJIPa4/iQWUCObaNVb/I133peGyN3Ox20T/UCIIoiTEI9sA+G\nPRwd6I4qDBPVOct8KkuZ0QYFKF4Qz5D31bTunEHAIvmJ/Q9/gtOPtB/Z7UtLLK530m6jXtP3/dy1\ny2g4+lqWWxt42i/S0BlSkkYQkKjRc2o0m3Dt6QRVYEj6/cE5Ovu8qtIAYOYKBuF0oskI3/nuXwMA\nvvv63+CYREQtr4b1La1iPxn28eGjA32uSYS7N8gU1/dRPJXTTh/vvr8NAPjR/QOsX9VtcBgmWGbE\n4P+X/L3zsb5cxZMd3XZWVspc6uhPciTEVrLsqaCeLkETBi0HXMKExKGCJOHbZm0dxwNNsXc9B01q\nj2Gcon+o/25e8nmDUSmXcEpSHWeHHVjE5FnfWEaTFI/TNEWvrd9pa2sTlv3v9oxmWXij0egcQ6ko\nlc1S0VutFi9qDg4O8M1v6s3JW2+9xX9/7bXXsLGhF8Wz+CnP83iD1G63GYdVq9UYAxPHMU9YW1tb\nXJIpZBrmiTCMmG20urbO5dBGpQzH1qUx13G5fBwnIWp13W8sCMaOBo060+X39vawuqrHpsbSGjJa\nNO8dHEGQt5qMIlyh75TLAQLaoNXrdcRUptk7eIJ0ottX03EQ0ga2F0Y4PT2d+x5bbgOjUDONG2YF\nG7SIWN9cQTAiPFQvQb2hNy2uvQYj1tfztc/8GlaX9d+DUh1X1vVic9Dr4sff+z4AoNvp8qLPdV2s\nrOjje3mAnBZQkIJFRw2k7KvqlRwEZLqrVIy1dV36bK02UF8q/FmfHYYwYVJbWFpqMkbQtm0QCR3D\ncYgJPUPb8/j749EIZfKCzbMEuSTXh3IJCbXlbq+HBo2vcRwxRinPMoSE+UoynNtUzC6sZiESRdtP\nkgTeBViZMteMviIUeXJKy4BRuEcoBZPGeFsZcCNSss8S9Oi8gyxFtYBYZSEu2wUutoeY1OhXJyE2\ncz3erJSq2FjVC6ikuYobn/4CAODW7ZcRE/uvfXqC4yM9F+483cWIcHmf/uxncfnmc3Pf46LMt4hF\nLGIRi1jEIhbxS8QnmplaapSYrZQrayoMlk8FNvM8Z+aaKaYaS3JGi8qAwcBI27T5+xI5isSjlBKg\nXVuaK+S0C8uR4/KK3jHfvn0DUhCrDhnGlGKE0psRvogLgJellMiprDWbKRuPx6xJ8+TxEwxIv2V1\nZYVX+Dvb23j55VcAAOura5jQ7lYIMU3HKsU7XQAMeg38Ehy30O8xsfHS1IPug4faeuXBgwec+nUN\nHxnl8EfDEY5Pjua6vyyPuAwwm5mSAsy8VEKgMEMxDEB2tXBeb+ddXKfS3qjfR5Ou/ZV7r8D2qOTQ\nWEZlRe/2x7mN9lmHzpwgJT+wIChhi8orVT+AZehnMxmPMZkUOj7TdzYaRxcELkt2C3/08H185zua\nJZnkgOPrrNnBwUlRzcV6q4Frl/T13L15HabSO8J3Hz3l698/OMXZiDIgeYaY3OxTlUJS1jGV0zKf\nAcl2OwCg5tj3XISxuFQNEK/o7EXTtyGHejeWZjlX0gLHZP++PJMwKHOU5hJjyk52QoXmhr73ynIT\nTQKvC8uETR6VJVvhM3e13pqT9eGUCSRr22hSRiHwAthUxvB8G7ThhG3Z2FjS/bVkGlw+/neJYufd\n7/e5D5XLZS6VzZYuqtUq78gHgwHeeUcTOobDIW7d0vY/3W6X/fU8z2OwcLlc5t/u7u7iwYMHAICX\nX34ZN25o9q0QgvWtyuXyOR2reSOOYz6Gkgo+Zb2uXtnk8szOzi4clzJHzSUejwLH4zyoadq4fVvv\nwF944UUYNLb6QRmFbNThyRmKxEKv10eFBDkNw+BMU5bnaLV0aXQSjzBgYeWMMyYCODd+PSsqloe6\nq8+11NwELP2usiRBuUK6VzeW0NrQ53r4wQN0P9T97J/85j9Be18DlJ1LATLKeshUotXU/bi5tMTv\nJ7cihJS5P+ycwKNS85X6Fo8Hti0g6b6qjTJnFMfDGD5dT61WRrk8f9YmTRMYlBleW1vDC/fuAQBO\njo/QJsYZEhOTkX5urUYdIWVPZJohVvq+4jhk7a1+b4jWih5rbctBp6tLotVqFUlSZJ3yqeCqW+L5\nyTAM7iuGMfXNnW2zeZ4zCH6eEDCZeJCm6QxjH0gpO1bzbNSItNJ0fbQoQ7cyStDO6DuhgY0SHae9\ni9590qOrrsFSpKdmDWCHuhpz5/ZNBKt67Dl1V/HZz+jMVKOxxkKl6uYdRDT3D0cjPNnZ0ceJQtQv\nkH37RBdTWZohoJchZ9gDsyqra6srnGKM4ymlXVOJSXxyBsaUphljY3KZMuTENKeTjwEgIsaRaQN3\nb+sBbX1tBVlHLyIGqYEeNVbPEpA0cti2w4u1eSJNUyTEIkxkjhukTrt/dMIsIMdxcEi18Eqlwh3S\ncRxurLVaA1WiZluWxYO8lPKc2STfo5im7QHNEgQA13Fw+84dAFq2oVDO3dvbQ5kwHuWyj15vPiZZ\nlqU8IAPgRZNeQOkQwmAFcUtl6J5omvZyyYZN6d3q2gZu3NbPZmW1BUkKoitX7yAiNs7e46coKp0O\nUlg0IF+/tI57d/RvVRJOF0p+CUnhGVaX6PULZevogsKrwIRMo7/7nb/FeEiYF2HCoevfWlpCLvVA\nlEz6ODvVz+FDQ2D3qcZGZRCoVfSCxSyvoWoTa9MLochw8+GDd3Dt2hV9fMMELwJnBWsxlQ75+4pq\noODfIPmMeIz+Cd2jzJHPsEuLkpze5OhrS3IJnwRl20cdXCVWmG9nCOokymsIZCSe6NsKBEHEaKRg\nFowjM0eTWE+iBqbwAxKKFqQAsFwrGErJtMR8wRBCMI5pe3sb169fB6CZfUV/cl2Xx6FqtcpluEaj\ngU1yCGg0GvjKV74CQLNEi3Le/v7+OSmF4rfHx8f4wQ9+wNdRr+v2MCuMWKlU2LPv475oz7qnoiz5\n5MljTMLP6XsqVZj92+300CEF9CvXr8MjNlOltcQG0lEazuBUM0SRbgvCMlmmxDAUeiSm++1v/w02\n16bPY4cmn9FwyAxh1y0Bhn5vx+09lKm9eOJixZBSzcfKpm6ngeHC86h8WfOggqKk5XFff//dXdjQ\nmKz+cB+lul7cSZEiJAmPXucMJuGhnn/+Hj78UOPV2oMj+F2SVqnnqKyU6LODJWL/pUphTBItEAIr\ndHy/5EOSYK1fCeA480+tWZ4jpWOmaTptC8hhCWImHhzBTvSYPhz0EdMiSGYZLm9qWEGzvsomz47j\nodfVnzudHpd6LeeY55gkTTEa6BJ6oy647SfJFDpTKk0xqZZlnYNOFG1vnpBKoliWGFCcHLCkwf6u\nxniAG2t6U/HS5iVs0ebqUreObVcPIF9LfVQGWsS1dLoN4RI85Pe+BNHXG5vTvSHyM/39h2GOB2P9\n+fLGFbxPcIzPfHoLwHQeLQSVHaeERx/p9vwX3/orfO/bfwEA+O//h//xmfe4KPMtYhGLWMQiFrGI\nRfwS8YlmppIog0MrXt8y4NX0yv+5F+7g5i2daVhdWeXV72gUTvUushwjEkUbhiGD65I44XLYaDhG\nSrtq0zRZcl9IcBlxfWsNn/mUdgkXpoAqUdlmnCCmDJRnSbgEtnRc70Ir8CiOEGfTzFSxE3Vd51x6\nu1j5t9ttBpFvbGwwcDuX+Yw1SsyZFdd1+ZnM6tyYpnlOA4SvJ03gkj7MytoqPveFzwMAXn/9dTz4\nybv6mJbJWb9nRZ4kyGezA6Kw3Jh6wRtCcdraUmPYud5ReZUAZfK+KjWX2PcNtoXrd3VqO5Q2dvZ1\nijYcJ/CMgn0UYTnQ93Hv0l28dEtnFva3HyMqtI0QwC12h67N7UIl2YXYfEIYODrSGcsPPvxwymAR\nAiXSjbItBwZ5IXolG4JKyu1OF4ORvt/+OMFpV3+uVqucHR2Nx1ySefsH38eLd3WJ5fKN20hmrB6K\nndO8WakLWeZYQwiLMlDxGBZl2QJboT3Qu9jMsGCLghiSIacrmSQmPBLkDMMIgrR5DGvMJRDDMGGz\nx2aOTk8/z3Kjiow0XTLkALUNISWUOS0nMMNHAkIWXngKwrpYmW+2RFGU805PTzlLNRqNuMSW5zkz\n+IrMEqB351/84hcBAM1mkzNQURRxaW8ymXAfPT4+Zvaf7/v49V//dX3v5fI54dAibNtmJuBF7XKK\nd7779CneeecnAIAX7t5FQeCr1Rr4429o8Pz//Y1/i+tXrgIA/uPf/T3cvaPbnR0NmCXZPmvj8RPN\nXv3yV7+CBulDlXNgQJpKW1uXcPPaDb7Xosx3/dpVKMrKP3l6Clh6fO9PYuZWZPOyhinMwEJV6HdR\nDwJ41OcM18IgIwiA0J6oAHDl0lX0It1G9o/3sUJZGLfXRudUZxweP/oIlbKuBvTO2lzNcM1pSefK\nCy24JCRcaniIiPkqpEKJ+q7vW7BovFGQnA0UAOLwYppxRdY3yzIk1DZdy8bW+gadK0BjoNtmFE5Q\nvOBBt4+oEAt1HASBns/Ozjrc3tM0Z6jKJIrZH9U0LZTKFT5v8f1ut8s2M5tbmzMsemNqfTbDfJ0n\nmtYJ7hCUo2LUIEnEd+KsIHGI+d15igpll1T/AXoZlR0zF0mZWLZZDqOv54e0VoK7pZ/JWbIHq0JE\np+AK3n6gn8n65ku4+5rO2L784qfhUMnYNoyp/64BJo09PTzG3oEmPOzsH0Bk8xNCPmEF9IzNhE3L\nRkq1W9t0sETMn/W1FX5h4zCaygOI6UBjzBiGasNFXh1+uQAAIABJREFUYhxJ8Gepcp68pMyRkK+b\n73soeYVKbIoBjZnVlSXcuqKxH71uF2ahvG4bsO35H1Oe5cw0jOKIU6qWZSPPR3TeaRrVdV0u/z15\n8oTvKwiqTGd1TZfLCUmS8MBrGAb/No5jKJ44wBRoQxjcOU3T5L9/6rXXeOD74MF7c3eMNA21BIU+\nE5tMC2HBJBFIEwq2QXTdYQceVdiu3byFek133m99//uIKbX9O//4a5iQZ9L2B48RJwVmTkAR0wbh\nEJuEc3j1peexXKXSz6SOwYSMMpOMvalsy8QJ0b1NS3EpeJ5QSmFnR5cmDw8P0e/oyaJWq8ClxUU8\nibHc0gO4axuYkFihKwy8SGy+TCkcE1ahOxnArxTYlRVEhO3qt4+w+/gDAMDW5csQhUGqYfL7xJzv\n5kIq724MgxZTmRwiC0kZ2yoxdilVFpLiVWc5wpQ8wEoC9aoelJJoDJWSr6JlQ5pTvGAhvjsaThCB\nvAirLkaE8cikhFGYGEOhmGYFDMb2SSlRLNMNATaanjdm5TpYrDBNeQFQKk2xIrOeeoPBgDctw+GQ\nF1xJkvAmrdls8mZptiw4yx596aWX+PPjx4+ZUWhZU8xot9vl6/k4e+oXxazX6dnZGf78z74FALiy\ndRmfevVTAIB6rYmNDd0ed49OMSLG0/buAYpq/epKHU0SxtzauowaqWU36svwaXJOcwXb1s9geXkD\nARnqLi0tYZPKTFkaYTzU7ejobAyXPET9So0X3PqZzj8JG64Fs9iYNSVyQcbuOZBQCVKYFizazNSX\nXGRn5Ns5ifHe21qK5X3TwWiox4NGo45HT+4DAA5391jQ2TdK3NfKDRs+bd5UnjCrzrNdNkCu1ssw\nCy85S0JFNP7JlFnr84TruMxIHvY76NK4JZMEW+T/2azW4FE7zWQGVQgbr6V4+EDLUfT7fTjkFTga\njZjZbNsWXF/PK1EU87zYbDaxTKzyQa+HQbG5FQIuzTGTScS4ZXOmD1mWdSF3kH8vzLCSFBsnAyWl\n+1C0UUNMc6RdqsPr67GhGZ1hovRz6AsgI4iH4dqQTYLCfOpVrH/pNwEAsVfHgPBul5+/DW9N/33/\noI1XXvw0AGBzYxOyWEDlciqjZAgkVPLuj0N2nkiVZA/BeWJR5lvEIhaxiEUsYhGL+CXiE81MmYbB\nDIDN9U1skUaLSjKcEiB7uVHhtKsWrpzaJfDOWwikRXbGNHmjowzznGJP4R1kGYIF2EzozAkAeI6J\nmBIBt69soEJlvnfeehfjIkUqJcwLIPpTlSOlFftkMsFwoFfgg36HfbFcpwVFTAIJMFMEUuLxIw2G\njKMU16iU5Zc8VIgBNRiG6LT1MT3bQk7p+TifAsOFELy7NU2TLXaEAmtmlcsBvvrV3wAAHOzuoNMt\nWHO/OGSasH6XYRicnjaQM4vNETkyKmmqJMILd18AAJQ8jz0JTbuEJjGCbNvHwYEGFUaRAZvE7yAz\nDGmn2zk9RkDv0HcduMRcXG4tAR19ruPTM7iUzbMbVZRpJ9cfThhgO08kcYzdJ1oTbDwYsGhk4mVo\ntjQQdZQDxyc63by51kSzAB879lQPzTJRJbuiYZpDUtZpNI7QpVLpoNfD3va2vvfRCKW6Pn4m5TTT\nOPeVXyDSFAW6P5UKZ30qP3kZytRHsyhCDn39mWlzOSGKQ5QDfS8ba1UIQbZNKkZOoHMpFeyUxGW7\nAzhN/e4ie8QikrZUkFlBQhEwVCHoKyAoy2nZAiYKAUTFFjjzxmy2rugTnU4H+/uaJHDr1i3OKM0K\neHY6HS7VmeZU0NCyLGbQzo5JaZpy1sm2bdafsm37HJi3OGa322Wg8dHREXZ3d3/qep8VUuaY0Dsx\nTAN7h7o88b/9i38Bg+7p7q3b+NTLvwIA2Ni6xKUc2zawt0/+fd0TznY3GnXcvfs83asHRbTmcBIi\nI3KHkgqmo7+/tr6OhMgaYZJNQfu1Gto0TpVKJUjK1FQCD6mcfw+fWxnrmEUqRMErErbAONXHN4RA\nrVxYZrnY+4nO8vUPYxyQR6FjW8hz/eNmvYVorNv7YNiBKISkpUBg6vuSSsI0dPstkw0UANQqVQga\nQ03HRERCwrZtIaTs93g0Qp5dIPtmCAgi3VQqNWxsav26Jx89wlFHg/7rjQYsHgkEJPVRwzBxhzQF\nR6Mx+gNqDwZQreosVZKmDB9xfQ+Oq/t0kkvsH+txN4ki9CjjqpRCvSA/eSWMhiRY6jlwCkFqIS5k\nC1RLTZySxpmzvgb/I31fV5oxSpuabecZV/ABlcHNUQCXhDezrQw3X9X+esKq4ifHOrO261l4cv8p\nAKCx2sLqiibyNJsV1Ff0uVbWBmguFyQEwdAZhZm+pgCL4CSe57EmpWkKCDGf/iLwSWOmkgSbRGm/\n9+ILuHpJDziTyQAZsQ0Onu5wvTbP5DkhvKJkZggFGIWwoANBNy+VYq+hJEn4s8ynquqe5yEcEpYm\nDXGwrY1re72hZqAACEomFE3og/EY9ea0Mz0rUpkjK9TWPQ+jp/plyyzhElSexjBt3VHD8RjFdCnz\nHI9JVXtveweTWA9Sm1evoOKQH1+5jnBCJUvLRUop6uFkxAtPQHFlSGOpqGw2gxtKeimuPq8Hzc9/\n4QtMD35W5NlUudmAgKAFlAEJmepO1+2dQlFZ9fr15yAMPcAenPVhEH7r2rXbMIjRFocpQAOsZ0kY\nhh544yzF8YlmX5ydnGKZTCe73T5WSN6iubqKKNYL8aPDFC4JCJqmA48WXN3+6EK4tzRNEY91rbxV\nDbBFwn9pmsGhfH9ruYpOm2jaeYZM6mfbG/ZhU6m234mQULsLo5RFD8dhgl5XD1xoNLmk2Ou0UV/S\nHT/LMihritsRF5J2eHYoSzB2wiiV0KEFThaOEdiEA5IJxkUq3PRg2bSgH6WAQWV21wDNUUikhCpm\nu1zg7JAGxsxkKr3KczaozY0UuVU4DQgWQ1RCTdmglskUZimzC9Eaf97CZDweMw6uUqlwqdx1XV5k\nDQYDXux4nsffbzab5xS8i0VWkiR8nFkl/jzPuYy4sbHBWK3Dw0PGT1UqFf6Obdtzl08KjI7+nQWb\nSm8fPdnFP//9/wUA8Nv/6Gu4e1urOL/68j1s72zrZzDswad3EicZOj29EHv7x+/ikOj4n//8F7C7\nq3FGP/zRjxAR1OCzX/giIlqIL1V9Zuie9dvYPSkWoBZ8h95zlrEwZsUvQebzGx3bHkDDB+JsDEVO\nCePxVHi1UW/AdsnYtmri+Fjjbs72EwxogVDyTAga09978C77fAIKEZWvXcNAi+Yny7ZgUElfKQWD\nSt9ROEaJRCZt00JCzT2OQy6fIcnZb3OeMEwBSbjPOIrQbOoNle1YePttLXLcHw3ZnHtWosAwDLjF\n9bguMt40SuT0jkzTxCTUi75Bf4BMUoktzZDSZkZJyb6HSimEtHAOo1TjZwC0lhqQZBatpGRB33li\nR1n4fkeXEaVv4Hd8WpTd/wEkKb6brgtJpT1RH6G2SZipl9cxojH4b/56D+/u64e+fMPG1z6ry9mX\nL92GTfOc6/sAYaOWV1fxEWEAu4MBtrb0wm1zdQ2mVZgqS3j0HjdWW3hMJf1Kpcxmy/PEosy3iEUs\nYhGLWMQiFvFLxCeamWp3j7Bc06voyeAYD+6TBUcSwyf9E9dyGMAtbJt3H6ZpoElguWarBUVA1DQJ\nAamPGcUZTskiJYpGmJDTt8gM5MTwGE8m8MktOsti7O3rnVdvMoGijEJnMIKkTImlDFiUrp4nlJpq\naCilsLWlwZlf/epXuQzQbrdxdtqma4g4a6J3reR+bls43NHPJ80dGPSqaksVvPYpLex59/INpLSD\nePfBe3j4RGe1Pm6dwv6GhuDdc7VSxeGBPv6VK1dwk/SwnhUiT/gaDUOxp5QpJE6P9I7weG8Xr7yi\ndwyleg2dIaXjLQuCEMfNSgBHkBVDlqNMYo+jKORMWixzTj2PR2MEJMk6HI1gFT5owoXjki8XBFxH\nt4VZh/M8Ty+kT5RmKXtFXttqISAGn8wy+JStGIYTmIKyhb7DwOhSOYAwdFs+6YcYkldduVKHYelr\n2z/cxmCkd8bVSoYDeg/Hxye4ckNnEf6+daU+Hk7Dg0WZr1D0kVApOxknWPeodOUCUUEAEA4cp8im\npnjwUGdcEzvBBpUEJkmKBrmsn56cYe+pLiFcXluDTUkUL6iyIG5qpXAooyBME5YgjSqZwTCmon5g\nNp+tqblzhhDiHAu2yFR5nseMvDzPub/EccyZqSAIOANlWRazQWc13zzP4wwUAPbX29nZYWD4vXv3\n0Gq1+FxFGcwwDLTbxRiQsZhnp9M55+X3i8K27almUJLALVhRSmGPxsH//f/4l3iFBHw//ZnXGDDv\nlcqoVPUzqDWXmJjy4P4DfPd1rYtleyU06X1eunKNS4qTSYi/+7N/q88bDpFRFtdAjve3dbtoLl9C\nFE4B4lFGrN8YSKP57g8AbCuDzAkaIICiZzgAfPLpU0mM7khn+Ya9DBGV5fMsh0UZot5wiPol/dv6\nZg1jnYhDqdxAb6J/m4VjlGl+ylUKn7SHTFPCKVg0aQLIwi/WhlmIUGcJj1vCNJlVPk9YlomEyoKO\nYzHg2/d9XLmiS1fvv/8+xqSDeOPmDW5HH9clYyFsqTgLmiQJ4sLzLs+RcUZJMOzDFILL10opbu+j\n8QgOfbZtm/8u8xzCnL8kfWrUEBMrc9STeKITZXg+kZCZ7nMIJBqr+vitOw56FZ2J+4snPj74AV1z\n/iqWb+pn8rv/6X+E27c1FCYad5hBKQ0HQaDf0Y/fewe//z//TwCAvaMjbFIJ9b/8z/4pvvwFzbK1\nRQZJ1jWTQc5Z8eXWEoILVAQ+0cWUkCE+In865djIQZ5CsGGbUxaWyWwWhXBCddMsRVDW6TdTWBCh\n7gCX1pfx+X+gH8pwNMRf/uVfAQB2Hn0El9hTrdVVprNORkP47rSDHRDbKoOAorRfmkuuT5ccFzmp\nzc4TH6eMFuy89fV1ZuS1Wst47GwDAJ7uHvL3lVLk5wasrixjnJLP1c4hM+/KbQd3b+pUpVt6Htcv\nX9XHXGqhQz6AJycn6PX0IiSOI6a22rbN3l+rq6sIqNPaAnj5lVfmuj8LCV+vqSQMkmKzIJERHqpa\nbsCv6FT12bAPRQsZO1co0bP3bYclG4TK4dIiKxECRbM0ZIxCglkmGZKQhO2SEAZRkg3TgkvsTNfx\nePA0hMHpaQE5nZzniHK5hJUVXW7beZhiUizExyO0CJN19fI6SqTwnckMDqXaPc/F/qEuk0yiDN2+\nbjumU8GYyjrdwQQVEvOMoxRDEs47ODzgid00TVafnnfIughVOZVTVqYSCqO+XhS0ggBSFGbFCmXQ\nAJiFyAnXUa+WcELl10u3NzHo6DY1SCbwCGN1tH2Ey5dI2LHpw/JowZKOgIL1plKAFuNCALEszHDz\nGf9HBZvKo5YpmHk1T7RaLZ50arUaP1ulFE8cg8GANzPtdps3GysrK/w8kyThRYvv+/xbLSSsr+fo\n6Ajf+MY3AGhB3AKD9ODBA3z9618HoMt8xSKrXC4z26rT6bBUQq1Wm1shfJaqbpomHNqQ5lIxK20Y\nhfjr778OAHjn/n0WH7186RKukXDpZprDJtjBJIrRJOPfw6NjiEKaRAIeUdtNw8DLd7UQ8OHeU7zx\nfb2Jq9WrGNNCRnX7sGk8bTSXGSsWZwJKzo97E1nCps2O5cOiUp1fcbjM1O620aa+ZciKVt0FkMoI\nVlEOUzZe/JyehNfWmvjwe/rZry9fxelAT+aPH76PMnntVTYClAhbZDuAYvkPg+cnoXLEke7feZbx\nWG85AoPB/Crvtm3ApI2E767Ao3Lt/v4eLl/WY321WsUbb7wBAHj48CEvvj3PmxG5jmeOaZ/b1Kd5\ngWtURIsFIAGTnqExkwRI05SPKYTBMi4K04WbmMEfzRN7pg2fxjzHEdihsuOB28WlLX0NS5cNlFb0\nvd9PTXzvbWJ495fxud/4xwCA2889h3ffew8AsLO3j4BK1bUgQJk2B8J10acF/r/5xh/j8baGUVgl\nDx9Rmfuf/6+/j+WGnqOuLpfx/nf+VD/zkYW+pfvi5SuX0U/mf4+LMt8iFrGIRSxiEYtYxC8Rn2hm\nKs0kxgRSfvv+jxHTLjZWLsJY71b8UgMlEtLsnrUxoayQ4zjMHhi12yiRmNbWUgXrW3qHtbrRQpnk\n/ff778KhHW2nPYBnFjpAOQRpWaRpBCmITZQrKAIQWjAZwN2bDDG6gBXJrH5MlmW8i8mV5F2p7/vY\n3NQ7hTwDjo50zjmKY9RJfO4LX/oiHnyoy2Y/fuc+VKJLWeOJwoO33wQArCwtw6aSUiWo4N7zLwIA\nPnI/wjfva6G+8XjC1jpBKZiWKBwXnTNiitQqc4taWqYx1RoxBVuACAjcolJhkkj8yZ/qlf4oHqN4\nfCJPcO95LRT4a6++xsxO0zTQp126MG0YRebLELCtYmc8BfanWTajH2RyFiAIAt6pJ0nCwq5CCFgX\neIdRNEJIdjyGW2LfplFuYbynxSdt0+CSVipzSAJVW44LxyusG8YQpj5OGGU4OSVwpemgUtUA+kn/\nhIkBr3//dTz3gi7J3H7+xanA4QWFHOcJR0gkBGD2hEAS612gfbOFoUslk+4QJWLkZe0JiurxCy+8\ngNMjytZ1h5hQv2ltrKH9WLfl5aCCazc1Ayc2+3CrM/6DtDNOpYAgzZ5MSaQodKAi5LIgcbgwzaJ8\nlUOq+YkEN27c4KyTaZqcmcrznMHfDx48OAccLzJEs6KdAM756BUxHo/5t4eHh+zTF4Yh//7NN9/E\nHbJz+vrXv45mU7/34XDIGS7Lss5lFeSc4jazelRZNmXSCdOARRnUKEuRUPmj0xug09Pj5nsP3ufz\n+36AEgHgL1++grV1/d5KQYQDGpsq5Qo/y07nDM/fuAYAuLSxiSGx/x4/eYKVNU0qqjfXMSyyM6GE\nR1meLAwvlEHt98bTkpYwkJNeWSaAcEDlv9zGaEjsT3OIqy/oDIjluNj+UJc7V681cfezd+ioYzSe\nkLWX5aJZ0u90b1+gVNbPtFatwCPChZSK7cu0hl+RmUogyQ8uh4InCgHPFIY5f/bt4GCPs/eQOTod\nzRIejcZcam61WgwZefPNN3FCumQvv/QSa6ApKXl8tyyL23uapjz+KQi2EdKEDipTzpS7HcfhZz4e\nTziDHYURBMEZTGHMLfQMACNDohUQXMI00CdB133Tw70XdJ9wqmO06TH8wRsCu6c6Q/rP/ul/gv/8\nv/gPAADvPHgbDz7SGmHf+vafY3VJlwIvbV7GMnl4Xrq+CWnrcz344AGTnupLS/BIb+vs6QH+n2/q\nOfIr967jh3/6hwCAZPk5+Dc1+9WwfBwezudZC3zCiymIEUp2gWGwEBCQQsV9HJPRasXJkFBKuLP/\nGBtLRNmtlBHSgH8axGjQQHC6v4d33/oxAKB9uomjY11+CGoChqs7cx4OkBbq5iUffkk/XE9ZcDNi\nKylM2QlKwLEK+rMxTYvOGcUgZ1kWYhqADMOAT+UoCHAnMQ0HNqXnwzDE0pL+e32lheBYD/i2maMS\n6Ia75Ac4e6rTlt9//Xt49IQwHraPbld3sG63C4cakFWp8EQQBAGnosPxGD0qm92+df2nJo+fF65t\nMc3WNAzGWjgG4Lu6OU3CAT58rPEjB8cH7HtUKbnISbit7Hq4QinsUqnEi2bDMGEUrLcoYSySUjn6\nY4216HS7mJBfXqnkcxnMsi3GsAyHQ8Z4fBxD9qzY3d3FBw81y9MxbVgFFsW3oRJi5w16uLKh08rK\nLaFPi47tnRO8944W4XTLVSy19MT06NFjjMlPq1FfwoCkCHzfQ0KD0g9ffwOS2uN/89/+d1ilwTOX\n+Vy1vovQ6kWawqSDWsJiA2dnYwWtmxpXsJECw/f1dUb9J0gJg9g/28PlDT3QvfXjR1ip0nvMbIAY\nq07JxsGh7ovlDYe9wqM0gUxpEW1Z8El12pJgk3KRmTDdolwrOH2u/cXmx0yVy2VuV3mec9u3bRsn\nJxrP9fDhQ+4frVaLGXxCCC63yZlJqlQqzbgauLzAKJfLvFDa29vjEp5SiktcsxinWW+z2YXUxRTQ\n1fmFiSzwLwZM6v+ubU0XrEgYjCdljtFIP5tJlKFPn3uDEUYER6g3lpATLT5OMlTpvuNhgkcPtVDk\naDTG7lO9iNw/PEJAPqO9/hA5CfF6pQBlKoOPJ2MkFwAE9rptFpaM45A9U716HSenNNYbJTAjWkxw\n7zW90BNpFx++r69t83IdXkl/Z5zG2Lit35U3aGJIm4pqo4FGS//ddi14TuFVl804PQApwRlyxDy2\nyTiHR7RD0zWg5PyL/oODfQQ0yZ8eH6NLcghBUMYSsXsrlQruPv8C//07f/O3AIC333wLd8hBIQjK\n3E5N04IwaO6xLFiKDLGzDIIYyfm0GvxTgiNF28zzjL1pkyRmjFjJ8+cWlwWAxMwQ5noh75eqSGhB\nui1dPBjqz6teE92RPtfhOANIkmJ9o44RuYQc7/YwGpJURpph53CHjp/iowM9Zv/gHYlL164CAPq9\nHgu6JnHCm4Yr16/j/gcPAQBX6y6axPIbuFWktLA92nmKjx49mvseF2W+RSxiEYtYxCIWsYhfIj7R\nzNTNSw3WexJC260AAEwHQVWvjdvtPQYR37pewdqG3ilalsCTJzrzcry9i+u/pgHTy7VLGIw0g+Ts\n7R388IcapFeru3jueQ04FKYqqCAwjQySpOkVFCyLAM55DpN0UcqlMpbIRqEclFmkcp6IomiGgWgW\nllcw1NSVWwiBoABNWw7bd8ThiM/VOeuiWdXXEHgO8lRnypZayzjr6FX6/vYjDMMi7WrBSKZg/VUS\nl8zyHGPadQ77fZydEDh6MsEyaXfYtok4LnRXfnHYJlhi3zRmhFGFgEDhQp8go/sQhmDdsFqtyiD8\n3b2nfMx6vY6UdKlc24VHz6bXH2FIjve9bgd9Ej1tLtew+2Rbf67XkYz1zj6OY85MDQYDzkokScI7\ntnliMolwRGJ2nlCoVfTu0/UceAQ6b7hA2dYv164HON7XmYjjs2OEJOTXP0vgkHZZkmTwCcArhIGI\nbD0cN8Devi6ljHojvPG6tr/4/ve+h9/+D38PgE4mFMKrFymR/KLoD0eICeFuZT4EkRHKThl+YWlj\nWggJFN6NJGeVh+02bMoEmK5Em0pBJycjXHtZ6/RM0gl2H+tneNe9iVJZP8O4N0ZOezjhZcipvG8b\nJjKi+Zkw+O/SNCHovCK3dJZu3nvs9xlIa5oml+iCIMBT0n/rdDqcaep2u7h69SoAbcdRWLwIIZi4\noZTiLO6sPtTGxgaDgtvtNme+AGBtTWcn8zw/J/45C/gtyoWmac6dJTYgEVFWKxcmTAKL26aAoGdp\nKckA4tyyOVvkOC6ikDIXtgOTzpmmKfb2dLZ7udnAq596FQDQGwww2NftWiYxToiR9+TxRzg80N+3\nbAv7ZIXi+WWsrhb3naBMDGoEGaLRcK77AzQEoWAMh2GIjKymhmkfhcxWjATlih4zylUXZk62Y4ZA\nmbwFG1WXWbkwG/BWdRtvn/TRpyx3OWjApYqHlBJRITwMiZwyTb7jIicwd4YcBmWjbGWzNZIAUPFK\nc9/j8eERTAJz1ysV3L6pM01LzSVYlKFNsgw+3UtzqYXnbunvPHj3J3jzjb8DAGxeuYz11UIny0ZG\n41+mAJOqH2Km3K2UZE/GMJGAmgrQxiSyWq6WUKlSOdq0uI0ppS6UijF9l7UbYdqsR/dBXEX7Xf3g\n1pdtuEXbVyHSRPe5+w/vo1yi8tzpGc5OdOnWtAwIymwnSrLtm4xTjKjELOMUrTVdCqwsL2Gf+uXG\n2jom1Bd/+GgXG0s6mzlJgb0PdNZ1MBphhbLN88Qnupj60me+xJTRXOYYUmNNMsn19TDMsLKsFwIl\nX0FhqrobGDrdmww93NrStflm3WMPsMhTCEgMzLVKaBTofhHxhK6UYjEzJYCcOmouDU6B52mMg7ZO\nD2fHGdfI5wlz5rtSSmbYSHmewloortqeCXtZv7D0+lU2Pa6WAhCcC77v44gG8w92jtlEOBlLkP4o\nHMeHRR0+y9JzXn5JPGWIGXTeoFTCtWtX9bVY8y+mTMGembBMwYspWwh4HtG08xQh4YzyLMeI6PXD\nwRBlXzf+k7NTLl2FScSTku+6aNb1extPQmR0nKOjQxwc605ULvt46009UG+urTHba9Af8kAxGA6Z\nCZqk2QxD9NnhWA5GJF3QC4ewPT1AKaGYFdhoraBEuKdHT0/xgLAZpXIFm5d1mezDR/us4O65JTgk\n/zAeTTi93j4bYEJUeFNpv0hAY3CKd+j63lz2fBdZaHW7fZh0Pd3OBAmVX8f9DnBIg2qicHJErNBM\noEnG5HUPAH2/5JSwf6Inx5NxB8Fz+pjLKwFWhH6Pw1EH2KM+Zxgo1QoWb870+RgCPRoPHNuBRVg5\nxy3BUkTZTixcwA4M+/v7XK6YZfOdnp7i7IzMtMOQ295wOGTfvffee48xUK7rYp3EHG3bZuyjbdu8\nSN/Y2ODvhGHIC6U7d+7g3j1t4r2zs8PHfPz4MR4/1mKC4/GYF1/tdntuzJSSU38xy7KnuJg8RUAL\nqDiK2efQsA32swNcxo/EWc5SHEEQMFbvg/ffx+mxxoxcv3kHQa1B19jF/rYufxwd7sGiMbTh1eDQ\nIq4WeAip7Z+enqJKZb44iS/E5gu8CkYkCRBNUniuXlB0+kPUG4QVmiTwAmLTBhmiPpVeKx6uXdOl\n8pLvwIgIT5TWEdMG87s/+EvUWzRPpBYiYt+K3OKFhuebyKk2GScpEhIODRqBnjcAOMpERt58hmMA\nav7xphQESGjxkiYpBn091peDMnvqKWTMnqvWanjplZf19YQR3n2g2W0/ee89HBMOdm1jfark7TmQ\n9E5Nw4SCvn7bBiStiDIlkJGTgaEETF+/x2b1PUoHAAAgAElEQVS9gS1igKZRzD6vaZ5DifnHm+dv\n3EFesNahcCh1ew+NKg5H+traYQ5Ife8pTGQE8XjjrbcwoHJzGEWYxDROGB4zRo8OjjAhGMXlraks\nh2M7uPucxsrduHMX3/jWnwMAjk9PIMkb+O33HiC8qRdTnu8jpfnKdW3uR/PEosy3iEUsYhGLWMQi\nFvFLxCeamfrMpz7HWSEhBOsA5XlWfKTddeHBp1AATpUS+MJr+vPvfm3MDB+hJO/apaHw2Vd/FQBg\nCAtBhW5PppD5VMuJ9TegWURFFCW2JE7OadJcBBRqmhaEmL3H6f8Vx8nznEt+CpJBqc1mk0HKAHgH\nLMQ0AxQrh0uHMgdCsm9QBjgVnWUZlw3yfMoiDIJAC0wC2Lq0zv5hs95jzwpDCAYkGsKYukUJgTMS\nIXz85AlCKrdJOdUMGgyGsOi3nW4XxXuO4hiVit5lepcsBCWdsk/TBCHpkYzGI7QJyPvBhx+iQhmu\n527dQqOmM0RhGPPufDIeM/tvtrwyT9TrTTSbGvj58P4BlhL92XAExpE+znJuo/NE3++3v/s2PtjV\nQO3W0jJWmnqne3nrGrp9nXXqdnuYTIjZN4lQKhWaPQ5MQx8z8By2kkiShIVpIaZt5++rzDccjuH5\nuk1NJgmERX3CzCDJGieNplkEy3XhlPUurVwxYCfUb/oKY19/HkqBjDSBjMBHjXbVMs64SxuOiZT8\n+9JxxBnaVOVIqP3GUcIluWg8wLins0gly4RVWEDMEYPBgPv6ZDJhcdTZPj0rnimEwN/+rQb2pmnK\nY8DS0hKX+QaDAWev2u02M0mbzSZeeEEDhH/rt36LAeh37txh370nT56wKOj29jYzCg3D4AxamqZz\njze5mvISbNNAiUoh0WQKmLcdG+NCJ88yIIhxluUKedE/lEJK1k5JHDOQ/u6tG7h/X2c99vf3cNUv\nLG8sZqDmec42WWmSAPQ+k3DCJUiZJhjGdA0yw9pSfa77A4BwmMExdWYyVjkyyiZ4ngWDxlmvbEOU\nCGAtIiYYrayZGLQTOlKKkx39DrvHAqdHui0010u4dkdnXh68vg0r09fv2zUUsGzPERgNKCMzTtgD\n1XQVqlV9L1miAGrXlhJMdpgnhBCcMeyennHZOcsytGIN6K8265xBDXwfIALTy59+FVvXrwIAPnz8\nEd58W5Ox9t86QrWpj7O0vIwaaTQaMGDahS+swSQUVxgMJchkwu3q2tWrePFF3a673S4LPe/u7iKb\nM4MKAJ9+4R7eeaBZePsnR1BEujGVBUXLkCgXUJTfUYaAoHb19OgYh+SDapomYhoXk3ACQdlsQwFC\nFuXgCc7oHQWlAK2WLvOtLC/h6mWdqfzeGz/ge7eFwsvEuG20lnD/kSYQPXm6gyiar2IDfMKLqSxL\nWd5ACMEGv/asB9msoTFwbtADsZKXW/Vzk0sxvRgQuH61qOlOvYmgfp6i9Pm/FhNVPkP5FGJ+qjJw\nnjmW5znETPIvnWH2FWq5SZrzvfi+zwufwWAAQQySKIqmat6w+e+2qQAaBKM4hOFOabHFMU3ThEMG\nweVygIDq7qYpEJHgXJqmc3vXmca0ydiWDUGq3kcnp/hX/+pfAgAe7+ywSa/ve5CSTG6zHEPCaUgJ\n2LY+v+uWIJXuFIP+CJ2OnmT6ozGiQjgRChktEI+PT3D/A2JZGBZay3qQzNMMS3XyTVOy0O5Djpyp\n9vPEpUvX8du/89sAgD9KhgjJyFWGCgdHulPfv7+NX7mnB5kbt59DpUFyAp0Bm6ha8FAlEcBhP0W3\nrTE4ubSRyMKoNIfnFgKkJiJStH/+xXso0YIiz8HtSCFnpQQlFBSmC3dxgUSz55VgEsurVi1hbUsP\n5kG9ygurctnHpKxPNrDHqG3qRWXJU0hP9YKiVTNxQpgTQ+UoUXswhQlpFUbjNhSVQ0zPQ49ESuUk\ngUkcvlhmyIllZNs2UiqZjMcTxBP9/VRKBMH8E3Ge59x3Z/uwYRg82TmOwwsPKeU59fFiwzMYDHgx\ndXR0hCdkgu04zoy8gM/YqFqtxmXEP/mTP5lhWJnnyu9FH02SZEYkUcy9mBKWrQ2rAQiZwsD0norx\nwrEdDCTdkzKgqP8alsN1CVNMPQYNw2B2oTAE7tzRivyn7R76tPiTUvI4Z9sOL2qyLEVG41Gexty+\nPNdCSpsQy7Gw1Jjf6xS5x8KhJdOCbdAmMe4x3qBcDZhxnZkWQnrXRimG4ejvD0YS9VwfZ+fpfQzb\neuz5wj96CSZtZp9WOwhsPX74woOgUrNnCuSElRxNRhiH5IgRO6i6+vtRnKIcEDYtS+Ba82M0R8Mh\nT0VBuczzULvdxuNt3da8coBTalNJGKHWoAWXkrDoOXzp17+Em7c1luov/+rb2NnXuMBer8dQiCCo\noE4wCm3qq8dmxzR4I5rCQE5MwNbSMpYbenEdRxFtgoH+aISLFLaWyxVco817t9dGnQS10zhDRvcr\nDQsxtUMpp0kSQwiklPSIkwzK1u/acRyYxRSfTOVyxuMxxlKPGfVqnT0Hjw4OUKZ+bwCsdC7zDILm\nB882kRKWu9NpYxTOL9i9KPMtYhGLWMQiFrGIRfwS8YlmpjT4eWYn/TMyUMD5Usas1UoRs1mU88cA\nkIrpZ05WqnMA3p91fPpW8eHceS9S5pv13xICSCkt7breOXHAXP70/ViWhbU1ndZ9+P5HeP+hTjcO\nh8Mpw0dJWEQbMYSAX9YrfEe6LNRjGAaX4kzTYJZDuTz1G6tWa6ypM+s39qywHYef5Wg8wiTRu7QH\njx7h3fe1bkeeZagSE9FxHM6MjUYjJLTzsE0TRQLQMG3E9JzOOl083tFlkbNeD0U+SaeU9YmjKMZp\nW++Qnh4cscCma1lwaZfp2Q6LqCRxPLclS3E9n/7VXwMASDnBt//izwAAJ0dH8Gj/sbqyBo92n4P+\nCUqu/vva7U1klBE9OTuBY+o72FqpQBHLbxILgJgtlVoZvquvOZqE+If/8N8HAHzlK1+BTV54RqZg\noxAuVGDakAHklPLO8oxL2fPExsYmewiKxEGVwPSWbzCz0vNKKJX156BqIyCgruUZTCRR6QRrNQ28\nHh6FqC6TLo5fKyz1YFgScaazHabnopwXYq0KKqfsgiWQ09u2bYcZaLEfI62Rm32cwHUrc99jNiPu\nCkzHitmxx7Ksc+y5ouQ+awslhOBMjxDi3PhTfO73+9je3tbPYTjkDJeUEsv0TJrN5s/Mcs9CD2av\n81mRpBkknb/kmEzWyORUu8oQgj0YYxgQRXkOgkt1s76VSikepx49+givvKzB89X6Etr9YfElOPTM\nGo06ZOH7FodsC2ZAMkvZ9z20C+KRZeKUMlzzRNlxEVN2wA0BQVOWm9bhkLiznTiYkLeaqAK2pfuE\n60kst/R4Ohr10VjWGbHPf/kuyoZ+J6uXquj2dDZtf2MC19NtvOw57M+KJMVqXfePuhMiaugxL7Uk\nUurTftlCKgpvuBRpXpQXnx2mZcGhikQWpzxuZVnG7ejx7g669NxUlmFDal0ky3OwvqzvseT5ePUl\nDUy/vLWFDn3/tNPGzlNdXv7w0RMcU8lMz1X0PD0bfmGfY0yZr8vNJR5T11bXsNTSz+2jnW2Y1gVK\nmVLi6iVNzPHKPnrE0s7TmMd4aZg4IShHu93DoKfbWxhHkAUkCIKrW9p7keAyM1CIJElYmFRnS6nt\nJQkqlEn2XBsRiQ3neYptKsV3xyM8/EDPu91eD0b4/9MynwDOYWxmP1/oOB9bhBVHKvAAwPkCnlJS\n00DPXYn+FtsLqZkfGYJpouoCJT59LnUuZV4YSUo5xS4BYDyPUgbTTR3bRsnXL/vWLcGlApllXHLw\nbIvF+SZxgoQajQkHinzLbGdqSOk4DqoVXWtvNOq8mKo3GuwDqBc882FRTNvG/fu69t3pdLB/pFkZ\nSZajTuaxnU5nRuTQ52uZTCZI6dko18NUI9VEj0Qs48kYxZpglCQs65ClEgqFii9YdVvLROgfVIIA\n1bFu/MoXyBJSvI6zuRXe9fElSoSF+MwXv4wTYsh0z76FFUqRt5aq6He0JECS5yAtQUwiyYOAZyhU\na3pQKpd9NG7rARC2i5xMPzuTnH0ULUsijvTnf/NH/ycmtMDMMoWVZV337/Z7EDSxN5YaWCUG2c1b\nN/k9zxO1ahU5LWQyYSAhbJQTeChbZAIc5yiv6Alo1YwhPP390DShCIdgWjmatj7vvasmCkhTKajx\nhKKsDBa160kYoU4uBbaaMmhN12E5zjiMYRPl3Pc0BRoA0igC1Pzlk1+EL5vdLBWfDcPgdmsYBi9w\nZhdfpmnydxzHOed2UCxgJpMJ/911XR4PhsPhOaFOHuQ/tnCbdzzMcokSseQqlTLjTcIkZ4mQLMvg\nEtYGpoM0JSr8JALNowh8lxm6SZIyTu7s7BSPHmnx3TvPvwif3qFhCIhc32vn7BgyJWana/N7S9ME\n1bLP95oXY7TlYjwncxgAVKiAPo1xsTaxBYBGZRmBr/uoaUj2fUvsITKhx5KJAipesSjwYdG0vbG1\ngaarFyBZGmOUFYzPBlKCUDhuCaCyYJalINUZWE4ZTuELqjKAGHA5MmT0TMZhCNucjvXPivbpKQQN\nho5pcbsYDPs8VbmujSDQz1MogZiwPFIoSALR3v/wIauS3751G8/fvau/k0u8+iu6Lz7d28fpmYYb\nnBwf4ylJ1Jx1ztDr6oWMaztIycUhkznKtDF2A5/nQ9MwL7RBzVXOmjrL1RpWqEzpWhaLZJquwyLX\nw8EEPVLrP+mcoU3X1un3MaCxP05SdrmQMi+mbBiYSsnEccyLU0MpWPwdhZzGJ8MQeO9D3c7F4210\nB1TGVQayeH54yKLMt4hFLGIRi1jEIhbxS8QnmpnKs4x3e4ZhMEh5nt3Yz/t/pdRMZkrO/sc0O3Vu\ngypmALyCdTa0ZsZs+a/wA1Mfy2r94hBCnLtH9pVSagoiz3NOQwqpYBe7YQgoAnauLi/hN3/jywCA\nk5OTqQBllmJAoneTSciA5SzPGVDnulPhy6AcoFLRuzPXmVrXOJ4Lr1TY2wjWJHlW7B0eYv9Iaypd\nv34dj3ef0nmqXM5ot9t8381mk9/dbHnEc30WbxxHMQbEYoxMATfQOxLTdqFUYc3jQBTlUwgkJD44\nnETwSyT46fpIC9YmTBYXtS33XFbwWZHkCU5O9TMeDvtIaId6etZDhUT0tO6JPtflzXWkqT7Z4yd7\nsKi9OKYNh8oermXAKtgmWYqTts46frjX5qzE5Y0l/OgHmk32zW/8CXrEIHK9ABXK+u3tH3BGz3Jc\nBGW9a/zVX/sM/qv/+p8BAL706//gmfdo2g6D15WVQZB4rT1DWJCmieCaznyVWy5yynxKZcOibIfj\n2AzwLLuK/f6kkfNxAANJpt+RDZPLTrlMWdPFt0woyiyb0uIMR57lMAswb8m7EBkEwLmMz8/6e5Zl\n3C9n+yiAn2mXkec5g8hnfzubXfI87/w4p4rydMRZhyzLzjGGi5gtKT4rDGGhTBnUVCiMiQigpGJN\nH2mY7Htq2Ip3z57jcOlEiQQmeclVAhsWvYdms471TQ2ql1CoUjYhjiJMxrp/ZPEIdZ+8z8oWZ7jj\n2EStqseXwSBFmJIenWMij+YvR9crGzCtQoDUQGaRHpNM4dA1W44BWQCpRQ1hSM+434Ol9Li2ttSC\nNElDKjXYkxWpBVuQ1c1wgr2nul+uLPkwaCxJJjki8jdMISEJxO9bFXikawgrgqC2bOc+i6POEwam\ngs5QSrMioTMmllWUTRVcGnviKMERecblSmJnZ1tffxgjomz2l7/wD/C7v/07AIAwjJg8sFSv4eol\nzWhzHJsrBcPRCCenuspgWRZOT/VzyITCIWmgjcYj7BAEQ+Y5C8POEykkTDmt9timbhsVt8awAtM0\n0CBLsvXaMvItGi+TGCPKxHX6fRx39fxwfNbG0amuDrR7Pc7GinxaiYrjBAllvgxT4IzuMU0T/k4O\n4Iy0zATAel4CAmY6JZQ9Kz5Zb74ZL6mP45aK1Pk57NTsL39Oyl5qUz0AgKlSVuHWfPLixybAC64Z\nmrkQUGJ24CoYglP2jz7+/I1m9l6AaboRED/zHhxrKvynBUULOrxEOaC0+uoK/zZME1bsjaKQSwtZ\nlp0rRRQ+gI7rMH3btm1eeNq2fQ4rYs4pTPqHf/R/4fOf/xwAoLG8xJN8HE0xAmmacsPu9/oIynri\nNQyDJ5PxJEJBtoySI56goHLE9Oxd12eRxnASs9FunmcwCDfU6fW5U6RJxiJuSZTilMpncZrBmfE/\ne1YMhj08+FCzBX/0ox/ib775xwCApmWg1dRlr+OjQxQ54yTLuFy8slxm+n6nN8QOUbANCCRJITNQ\nYrzH6rrDk8X6WhMVWuA2lwc4OtIDYBim6BKWII0T9lqLw5gH+X/9h/8anluwep69mEpyvWgEgERF\ncAi3lWcpMjHtoyb9PajXIAn3ZAkHkkqEqUjgB9PBxy3YNcYExUMxDRsW4U/sUoklHwwDPFkk0Zgx\nPKZh8gJKKckYtDTN5i5HF9c/G7Njy6z6+Gy/+Vm4qtnfSin5t7N9bvaYhmH8zL/PLrg0W+mnx4OL\nlPlsy8aAMDVCSNTJ8Lbf7Z0roRfvQeaS6fWmaSIqmEp5AiJIYW15CcuED0ryFCPauPlBhQVlT0+O\nMR7rCW15qYHlisbOBK7J7gxJmjIGtbnURGdCk3+WXEg2wKvYMKgNmo7N5vUizUF6zphkExhUevPg\nIY309ZdgIDVIqiN3UKVSYMUL4Cg99mUWsLamN4G3rkuc9fXkrOFepHrulVAtk5BwOkRCGyc39VG1\n9DM3vRypTYvjSoph2J37Hk3LhIGiH0SoEF6pgoDhD0IBHo0rYRbBITylMA10yBkiTlIoUn//0d+9\nxWzK46Nj9Ok4juMyHmp9fZ3b2tLyMi4Rpunw6BAHtFh79OgDxtTZlj3DdjV4ITb3fdI9mqbB5cIk\nSfk6BQQLcBvKYIFp23dQ9vU1t+rLuEqLrHEY4ogWRzu7O2gPdZvshROMyZe1NxzgozMag8sOHh1p\n7Fgic97UZXGqtRXoKqYbNsVeo/PEosy3iEUsYhGLWMQiFvFLxCecmfrZqfbZ0ti5b8+w6nRKmnaN\nxvnjFP+S0uAs0uwOT2FmBZ0LFDkvIabgb6lyQJFmCyQUsaeEnD8lDZxP37uuy9k1ObMr1dpPtMOS\nEhYz9aZlhtld8uxOzvU8BqnP7nRnned/moFIx7Gdc9koj0p+F2Hz7R3s4zvf/R4A4I0f/og1oRzb\nZRZQq9VCSuWes3abwfZaCJHSr1GI3V2y7MlS1Gp611itlzEhTzTPDeAQYyTPJS5dvsL3vftUp5tL\npTJM2mGMJyG2d3TZcTvfRkTvTlkme1/NE4mMQJZ6WG75eO01rd2yVS2hAr3T3ajneNLRO/vj8QQV\nEtu0VlwIyrwoKdHtUHYsnDJMlgOB5ZbOTF2+t4V1ciy/deMm7ty6BQCoBGVMKPU86A1xeKB3YH/w\nB3+A73znOwC0OF3x/jc2W7h378W571HCZDsIZaSwyI/PFjZroKVphklY+HslcEgHyjMkBAHBTU9y\n5jPLMljU/8ZhyCVa0xKQRVkbWuMM0NpMhuHRuaJp6S3PIagM47gWBPVF1wWLYf4y8fHxJmUQqzyX\nmZpl8M3+/WcB04GfDWqf/fvPK1HOnuvjv/1FEcURj222bWKddHzSTCIk8K5lewxMN2f0tZIk4b7o\nCsClcTDsDXE80m28O+rj6m3tNzjqd7BDJf0kiVGmcky9VoXn6fczmYyQ05SSQmJAWVMnU5iQBYvh\nmFofa86I5QCyABOPcmTE3G04deQxPU8HSCe6vcRJAt/SmaZLazWsCN1+7bKDCmWXVJIyIdYxDEwo\ni16pljCOCJaRG0zQiYYpJyga5QpnkZw8mHrbyYhtWiZhH5Y5/7wx6A9QpXK9bduYkA3WyckxIhpT\nK7UqJnEhhMzFGAxGQwgi5vieg4SeSX8wwje+qVnIaZqe01ubZbLOlqZrVGXo9XoYs7WTyWw+x7FZ\nV83zPDj/L3tvEmRJlp3nfff69OaYI3LOysqsuafq6gbQkwEgAYhsEiCNA2ggKYky44JGiTuZNtJO\nJpNoNG5kRm3EDSlxkBE0ghAMFBoNgI2eUPNcWZXzHJGRMb54ow/3anGP3/ciK7PypbKtQTP5v8kX\nL/25+3W/w7nnP+c/j7E0Kpx3HkAH2ntOR9mIRGjWMAh98pHWwaGxJVMGodaEMjc0OzHL8tzOHT1O\nVxIbDoqMHfFSnb98kXWhAl99Z5d+OX8o7cWvi/syactSVkEYQW12E+mnakxNC2zenwp8SIpAvtdW\n+Uh8C95qshx2o5dBUFYF3rWs1ITmsyqcTOxKoSWFVTHyqqnaFJ5+sNag5ElrM/vkBiLUOZVJEE8Z\nLIcESKcMq+mMwdKomaYQkiQ5RDOU51QoXzewKApvzGRZdkiI0GcCTsVyFEVBWNIwqJmNxqWFZRJJ\nod3b2/N1pE6ePOUpvNXVVbqSEZFlqae9kqTuU1n7/RE3pNjsaDj0YqKrq2ueIoyjBFXGTEUhS0ui\nDp5l3BYl3tW1VeqS0TTo9f2z7B4ckEsHOHL82CFj81FYnFvAqMl1T51wcUMq7aOkwGtydBctMU0j\nlVCT+4+jkEQKZteSOnEoGThG05Dn1mk3WBQV6GZniVqjrEtY8+820BrlSlGiteYLX3J9c26pxZe+\n4owmYwyLIqj3/PMv8NLnvjhzG02aYcTo11aTSkZWgWVBYmNskXvaoMgNtlT2jw/HEfb6IgNQFCTy\nLmyhvVBgoAMoSiHIAC2Tf5GNGQl1GAeBd/GrAlIR0Ytrcamm8lgUWHn8gz5Pj+dpAUpjjDdqHpbN\nN32e6dimh80R9xtVsxhWMyugFxNh2jRNuXjJ1frLcoMpx01S8waUMtaLjwZBMFlgtUWXgo1FTirZ\nUvV6zVcs2Lh5lY7E7Ngo9mP03uYm+2EZogFKitRH9TZ7Uk8tzboYMdZya0ni+kztAxiOUqKajKe4\nTl+M++Fo5ONLCXOUdotqI27RCN2Cn8QjVEMqIoQZucTtxUGCkWLF2IIgce9k5fg8c6siD5AoSp3f\nOFAEslTmqcXKij8segR16cthysHAbQKtGhHY2alMC57OM1nu6dTMQirz8tVbt30ASzoa+2NcnKi7\n0V5/6EMhUBMZDGOMFzgNptba3FqfGdwfjjiQuFUdaEKhEcMwQMucXRjFYFTG6FrCxxiLWZ5TRpIE\nKvBK+VEU+TXMWnuoDu70+NOqPObwuCnXtnarRVsMKx1G6Nh9/9JTZ3n3ww8A+OFrr5JKDWClFJlk\nreowIIzLNWra4aAo9Oxrf0XzVahQoUKFChUqPAHUT6rWV4UKFSpUqFChwv8fUXmmKlSoUKFChQoV\nngCVMVWhQoUKFSpUqPAEqIypChUqVKhQoUKFJ0BlTFWoUKFChQoVKjwBKmOqQoUKFSpUqFDhCVAZ\nUxUqVKhQoUKFCk+AypiqUKFChQoVKlR4AlTGVIUKFSpUqFChwhOgMqYqVKhQoUKFChWeAJUxVaFC\nhQoVKlSo8ASojKkKFSpUqFChQoUnQGVMVahQoUKFChUqPAEqY6pChQoVKlSoUOEJUBlTFSpUqFCh\nQoUKT4DKmKpQoUKFChUqVHgCVMZUhQoVKlSoUKHCEyD8aV7sv//ff2S1mG9KWcDK/yiUUv646c/+\nOwvT31pr/b9W/sNYe/j7B3w2xmCM+dT3hTUP/S3y+Z/8t7/26Ru7D4O0sEVRABAEAaPRCICDgwNW\nVpYBGA6HpFkGQNKo04jdawiZXHdnf0BnruPuLc/pDwYAdDod8jwHYDxOCUP5baj98ywKRZa6e8gK\nC0EEQByHZVMwhcFa9zIyo0jdI+Hp5Qc8/Cn8ym/8V/av/c3/DoCF5VPUapH8T0Ahb6gwGYEdAnD9\n4nv88I//g/vewuLaKQCWj5zi5OlnAIhUg+GoD0Caj9FBDYB2a4F2pwVAo1FneO86AL/1m/+Sn/2z\nf959P7fIaJACYLMcRSrtHhCF7hl88MZ3+L3f/mfufj689sh3+K1f+WVb9kFjjO+lYRhSq7l7i6KI\nLHPvIRtnNGP3vdaKHPcwe4M+YeTejwKQc2bWUqTuPkOT05B3aJUm0+6YsS1Qxl05QoF11yqKgkz6\njrWWpeV5AOr1GkEQAPCv/tlvPrKNf+8vfd12x+78rXoIofvJYJz79ia1iJqWjpFb0O4+G0lGP3VH\naRVRKHfdeqSoB3LPFnI5j0JxsLfjrtVu0mm15T8M3aE7au+gTy1w/bGwAaFMTakdkVPILWjaSQzA\nP/4Xf/zINv7qn/8VWz5z3/EBrCHS7p6DIKDddvczPz9PHLn+HCnjd5paa/9sjTEUMn8UZjJnKKU4\nPHImfxyaSwSFtRT20/OQKQpSmT/+yT/9Pz6zjfuDkX37w4sADIdjYnmH93Z2aHTc3DHXanOwtw9A\nGATE0h+jUPt29/t99vfdMVEUs7y8CkCWF+x13fdWK9ry3lr1Olub9wBI09Q/mzzPKee+ubk5Go0G\nAN1ul4ODAwBqoeLnvvIlADpzrUe+w9999Ye233dzw3g8pl535xxkKUfWjgHw+WefZ2/fnf/3v/d9\nLl2/AsDPvPJlvvbylwFY7nQ42OsCcOPKFUbDnjuntpx75lkAFjsd6kkCgFKaQl7o9197g//7O98F\n4KmTJ/iNX3Vzz9rCIlpeqdbKd7HMWAZDN/8ttBqPbOO//u7b1kpfmJ4nFBYl3z8M033qU//3qQ8P\nx/SaO90fwT5wPXbfuxHyN37lZx7ZxisbmzaQxT8MA5CxbqzFmkkby49ZXvh7CLQmkHlRB5pAfquU\n8m2bPo9Smmzs1t1er0u7PeeuG0UoGfd5YShyd3xWTMa0NRZFObcplNzzc8dXHtnGyjNVoUKFChUq\nVKjwBPipeqYUD7aklbKHNo4PtIStfVppJ0QAACAASURBVKCBba3F8kij8aHw93O/N+rwpWeG26G6\n+wmCAC2WbZHnaPk+iiIGQ2c5J9bt3N3xGuN3IpP70UHgd39KKX/OIAj8tZSyzn0HaKuIIvE6FTlZ\n5rwgSRz6XY8FVOA+hxq0v27wiPZp5jvOw1ZLmoSB24mm411u3boubU1paHfNj9/5ET/3878GwPLa\nU6SZeMxyS1G4+x3lKQPZfQ6GB0SB2x2GxmBxz8nYRdqyKw1DGI3dLnO5cYw8dd6N4ShFixcjYMwH\nr/8uAD/47m8xPNj7zHYdbuN9XoWpv1PxKGmt0bJbCpUl9F4GRWqc5yhOYv/erLX+txbQoXg6soJM\nPFlaazLj7t8Y4zxSQBLHGOuGap4NwJbvX7O/53bkO9t73ks5CxbqBZ2aO74eW7T8thdaUO4eWk2L\nKvugClGlByKzBHLPURh4J0wSKqwR76S1hIHs/IZjv4McGU3WF8+atvRH7nNmNHkuzzOB3IzdtUxB\nGIm3S4VgP7t/TmN+rk0gO1FjDVY8faPhkFy8imEYEsfO2xXFMZE8h0Qb33YANb3tlD+U1hMvNzzW\nLKSYjPtD32tNMON8o9XkDO12i0bNtWN7d7ecCgDo9pwXJlCK5eUlABrNFlHivKlxYWjL8fvdLts7\nO3IzirF41hcWF2mIV9YUhlbLeYyttd4DVRQFu7u7gBsnpQc1TVM/l9VqdT9/zYJ/8W/+L++Jj+LI\nj6dChywvrgDw8ccX2Np2171w7TqZjL8fvP4atzc3AXjpmWe49MHHALz6ve+BzBNHzpzig0uXAFhc\nWOCFZ58HoNVs0Rs579Jrb73JcOSeYVakpNKPLt6+zdWrVwF44dwztOWZ/OiDj7gnz+Hvfvs/e2Qb\nNQXl6qZgJk/S5Lef1evkRJ91SPl/1lAUZV+2aDXl/TnkZZ14U71bbgaEQeCb5bxCpafd+HNiLXmh\n/H05tgX0FC2l1GR9zrOcvng8d7a2ODjoyvkzP3b3d/c8u9FZXGLt+Bl3XaMw0l5jrF93tVJoSo+Y\n8l6wmdo485E/IUzmJzO1Rh1+28a769RDXIwPpvkeRu0ddltOFktrJy5Mdd/UdtigeozezeTF6wnz\nRprmflEOwxBTLppFDkTyywmlaEzhX/b0Yu5oynLgTVugBjD+8LITaI03NpRW3o+a5xlRPOms6Xgg\nJ2p/dvPyPlqMMKUhzZ0R9L3v/EveePX7rn0oQusmtHw8Ym/7DgC1Zoda09EPyytPMb943F0/1FBO\nVrtbDMT462/HdOYX3Pfzq9hm3Z2zyLnw3p8AcPvSu5x77uvuuqpGOroGwHtv/hY/+O7vADAcx/zS\nr/79z27XFOIk9oswOBcyOBpjPHaL/HA0IhaDqBmERDIYM6VQkRjNOvK9ygJjMaaMsW6CAAgUqXV9\nIbYBgRyvlaYm9JkuHEUKoHXk3OQAygpd7ow4pWYfzvUYv6gpBVq7e1juuPYD1MKEYVpSbBMqMM0j\ngnAoNzQZQ4VR7B24ftQbpzRbzigeHmQooQXTfuoNE60UYltj86D0/JNlOTW5n7zQFELdGmV935sF\nc63mpI16suz0k5hB391/vV73xkAYhhODVE1t0RSTdUlZylsIrPUL/fRcUv59/+dpw9xaQzlxTYce\naK0IZjQYLRyiH0tD8MSxYwxGQiMHASurK76tiRiOxlp2dh2Fl2Wpn5frjRaRUJ15nnujqVGvexpF\now4ZUNNhE6Vh2ul0GArVdffuXf9coyh+6Jz+IAyGA7/Ip1lKFLnz15sNdu6uA7B57SapGMephUD6\n6fbdMXsHzjC8fO0iY6ECd3v7dKRdgyzl4s0bAKhbN/jk6jVpY+Bp2L39Xaxyn2+t3+IPfvwDALqD\nPu998AEAfzH/ZY6uHAHgx6+/w2Dsnj8zGFPqIZ9nWXW0VocOfNCTVWq6Lz78XON0KOcMiGp1fxPT\nvzFT89njwBpDZsqxMrlTtymdzFvl8NNae95MMVmz83TM9SuOxn3nrbd4+623ALi7sc5wUNLBI7RM\nJhbFsaOOtj751Ale+MLLALTnV1hZdetPPs78Wnr0+ElC6f/WWLKiDFZ4NCqar0KFChUqVKhQ4Qnw\nU/VMWYunou63kB/M7D2YwPsUVfgIHu5hXqqpDedPDFlmQLxOKgzJjbOW9wd3SHMXLFyLWhS5u3J/\nOKbRcO7zUAVYsW8NmkLuM9QTH1Ru7MTbYTW5Kd1y2u/sVRCgKOmNjH7fuerHqfHBrYPBiFbHeaHm\nWnW/q30UhoN9ersbAEQmYHRwwbVj/SPo3gTg7s4+JV0YxzWsUDaNVoP+UHbM0RzHjjqXa72WsyrB\n9ieWl8kDd3yhYW/TudG3r+ccDFw7Ll94i2zsnmszahHJzvXs8y/yo+//FgA/+I//nly75/2XfuMf\n8Au/8jdnah+4LpqXCQJx4t3N0zvqctftvtcMxeuU5da5fQCMIc/d8x6PRwwH7jdWKaJEAp2jwNNP\nFkWoyvdvvGeysIUP5tZa+12+MYX3cEZhwuMQTdZk/pwG7QLMgZgYJTTrKIf9vjvKWPEgAsNUoYSL\nskXug0ajSHFv4J7b4MBy0E/leWrma4m0BawEfioLOnTPKjWWXOgZW+Q0m9J/ktB7CMaZoZePZm5j\nLYmmglgDT8vGUYdGvemP87STtT7g1KpAiKBy9yzvpShQkgygbeHfhVLqoZ7wB4YPWAUPCC62dvZZ\nyVrrEyKMdeEAZVvLYN9mvUFdvEjNdot+33kO09F4Mr/EifewNRoNhkJvjbOMppz/YL/rA9brrdYk\nBEFrPxaGw+GEhisK7706evSof07t+oT6ngWjUU4unmpTZNTq7n6yfIw4L2kEMU0ZTzYdM87d8Wlh\nSeRd7ee594DEnQ6dlgtKjgswyvW7kbbc3HBzmM0L4tidU6G89607GvL+e++7ttfqELj+e+HaFbLU\nzVvtyHLv5u2Z23io78z8KwerJ8ufMYWn/UKU/6w9cfXpK/g+qQwUEsKg4imqWWGF8TBYyqFiphih\nWRBEEVlaejDNob6vPIeuvBfNmMytpThP9ScfvAvAqz/8Ae+/8zYA3f19T8/td7v0hM62dsLGBJEm\nlgSvLMu4dd2FooRhzMLiIgCtVtv3yVNPP8Pxk2fd3egAmZr5/JmTj2zjT9eYOuQzfHS3me5kn3X4\n5BD7aUPrM+/nJ4+8CAiElgjUkJsb7wDwne//a9L8lwB4+tgXMcZNTKMRNGUQDvqGgSxevdGYXN5k\noDQHPeeiJuqhZRXMxhndTCa1uCAOhY4aF+RCz/R6Y7Z2XCc7GAw5ctxl0w3TnO4FN3HMtxNOH3cd\n6+jCmc9s32g84NZVN5nczS5gh87YaTLmmGSW9boHDMflApizKfEM0d4+hbRV29ucXpbMuCTxkVqt\npEMg8V6FsmTiLh8M97hz8TIA/XvXGY7ceWiv8c4bjs67cPE7XL/oBkurcYZv/8Y/AOCbv/irGBt/\nZrumkY3GnkoNrCIU2ssa482VWjIxXjIVMBLTZDzKKMRoSgcH9ITT7/cHFKWRHcXUxICuJTW/ILYb\nTb8AKau8cYFS3m2tgEAM7kCrSYwBn53Zcz+6uULLIqIBo93C0R1B2nUGSxQrbFBmaxpM5p65KaAZ\ni0GhNbkYYjYfMieL2qCX+8VlrhGw1HbG1FyUcCAUVIZFB+48SaAZ9d3z0XFAozUxcLTQXiMTciA0\n6yyIwsC/x0MZeSgGYtSXmWIAcRwTCU0V1lp0Oq4/z88tsDDvPu/u7dHd2vD3HJSGNoosn3pfU0FW\nE2NqsoC4ubCMd5yEM5ipPvYoTMfImamYvHQ8JpK2NptN+mMxdsYj6nVH3zTrDban6Lmy3fVmgyBy\nvx0M+t4+Hw6HrC47unCu0/HZyEVRkEgGXBzHPmuvKApP+TUaDZpNZ7wGtnismKmji6uM5P4bzRqF\nUEW7B13ENiYb9Gm3JS6mpimzVI0xBPKelUqoL7oNW96BTPjlxnBILqEKqlXzGZFxLaYpVFeRZuzd\n23bPHMVictTd2+mz3Jasxo2bt2mXMYKjbZYenaj4E8GhkM7CTGKdpmJ9PhWw4ucJNeWHsD5uy1rj\nQxv0VMiUlaPAGVYP9IA8BNevXqIu1Gqt1iCUTVSB9nG0RZH7OFqLIgjdXLJ5d4d//D//LwB8+N67\nJBLrF0YBi4suDGQ8HDGW+EulA4Iy9jGJOHnKZX2eOnWCXM5f5IZCjO7hoE8qc9Xm3Q1e//EPXRtN\nwfMvfh6Av/xLv/jINlY0X4UKFSpUqFChwhPgpxuAbidW8nRQHHwGU/cgnRjuz/h7AIV3n25VuUse\nj1Maskt6WIbgk+DjS9cx1nl8Bv2r/O53/j0Af/L697ny8ScAnDn+Al96+c8BkJk2G3NuBzQ8GHLQ\nlZ1UGJLLTlfhAp4BGvPzBEY8N1nKds9lqRXKcFK8TqNRSjqWAF5j6Q2da18rSy1y5y/SIfnQBaC+\ndfEG3/l9t8P62X/0P3xm+9J0yEHmdoovPf8Kty6789366HU++NhlxQyHKY2G2wWaIicryow/KMT9\n2m7G2EDc62mBUnNyvzmBcjsSYy3L8y77qNNYQrz9bG9tsr9/C4CDaAcksPTji+s0a273/PwLr7A8\n57x/436X9vzaZ7ZrGkU+2W27ZIEyaD/3dEitVvO7QK213z1vbd9jV7KhRgf7/h0aYybB0EFIf29C\ndZTXml9YYHXNBUs2Gk3fh7WaUHtaaUrlK1MYdKmTNhUIPFMbjSGSSOoAQ1AXGmuUU2Y6xXHkPWXj\nYuTbEiaWOJpknSJaVGZoWJKg8/2+ZuvA7T5X44BA3nWWQlBSSu0aY0m+aNQjxrFkC5oR6UieubHE\nYUl3ZoR29v2ftqFLugBx6blnOOwPmNYuKueMLMuwQoMtrLRYWXMBqgsL83REt2mv22d9273fMFRo\nuX+lA3oyFufn2iyKtk2gNVY8khbjKZPAFCBeFiz+PhUKw2zvUSm8N2yc5QwlS9Jkufc07e7uYEst\nujgmqLvPzVqNocwLg0GPRsMd325FDEbuvubmWyCezzTLvQdSaeWTCEIVUu7J46hOvS5ZnnFIowzy\n7vfpSXBwoi3Qmql9ANnmOsdPOIrlq1//BsdOus/Xb17n0kcfAnD32jVWpJ/WaiHj3D17qyIymSuz\nOGH9wHnou/19l8IMtOOk/IhNB7RbztNRr8/zjZ95BYDjK4v84A+dztTlixe5d8N54587e5bOaeel\numaGSIw6W5tbPzUvRWEKn/UbmamkJI3Xu5tOrbKH1rwpGTYs2VQmeVFqFho7Cc3BJYFQ/vsYnvB/\n/k//N1ZW3Ny2tLrGomiZdRaWSBpCHzda6FBo5YN9UvFCr9+66enpOKl72rowhtWj7vk36jW6XbcO\nZFnO9o4bi/2Drn8mSS1m84ajX9M094Hp9YX5ib1grU9m2N/fJ4pnN5F+usaUmnZh60Muxmln5HS2\nnT9CHY4IKRcUi/WZFocupQOQRTyyKdcvObqtX8Q8/8Ln5aSFFxzEqolA2rSQmH28TvPDH/+QsTkP\nwGtv/S4XPrwLwFzzGFo7Y4dwmZG4NlvtGu2WoxBOHDnpO/44N/SELiqsoj90Has/zhj2xD2ZpZQL\nX0jIrZvuWiqI2Zesqu6wT1q439YjRe9gC4BsPMaYmlyrYKCSmdq3u7fHYCjyBrpOWncDYfHUWY5I\nbFY6zunt3JP7SjmQ+x1lljLEa2QKdvZch1+bb9OWNP0kANIy1qagyN29R0HCmdOu8w/Hz6Eidw93\n7m2yueGMQmzkefMLF95lr+8+f3Fz3RtTv/rKX3tkG6clJ4IgoCjpG/CUnAVPM4yHfW5edxPszs7u\n5HhTTElXTCjroLATAQol0gHAVrrJQdel987Pz9OWBbzdahGKITbKUwq/EVE+OznP88eKRdF67N3o\nLqJCDCUUTVlYrc3p9939ZDYnkosF4YTSsiagKESMj8RPzrVanfxABDkH0JDuleUjRmLoB0nAQBbo\nA2upSYyKMZaDvlCKRJTzWVoY0uwx2qhCH9ehwoDclNlZXR+/hlY+vsgYQyLUztraERYXnSG/tbXF\nu+8KtX33LsO0J7+FOHKGQZ4XdCU1e5BnKCNGS71OmRgahnpC11pFIIPBBOaQwT5rAEIQBMwvuLlj\n76BPoNy91+PQiwX3hgPqjTIjr0mtpKyLMYFyz7jXvcXGHRmvoS3tJ4KwRavlxk17fg4lm5zCFGQS\nB2lyCLU7p9Ih9ZbbqBbWkJap9kFALnNQPhxSFEsztQ9grZ5QMmaDe/dYfPY5d57FZTZKodyVFeoy\nd3fCGCQbK0iaXLnlMv60Ctm55+a+/X6XaEHuM4jJZfwtLy4yv+jmmK2tHsdkI9ffusf2LZfxV/R7\n7Mt81ru3wd/6L/8OAL/57/u88baL5anXGlg7mTMeBfMIYc4HwZa0sHV9CSAsYCw7ziDQU/PELP3J\n+jU1t4bcThlipRyCwscQGeynHCKfheF4yNWrbrN99eoltGxsWq0Ox06dA+Dk2Ze8SOyf/NHv0+2K\nQdQ/8JuNlaPH/XgtTMEzz7r4ppdeeo6hyA1Za/n4YxcS8h//8HuewhsOBxjZyKWjMQeyPsRJjJE4\nziDUXlZobr7F8lJn5jZWNF+FChUqVKhQocIT4Keczfdp75P7/uGxbNO03YPs4PuD68q/iyIjEss5\n7e+wfsNlnZ39wrem9JkmLs+fVLhgyBiJf+Tu+pjVFVfO4Ctf/rNs3HW7v/cvZHxy43V3n3nG8ryj\npn7hm9/guWddAPgPX3udgWQ/7A8zwsS5NttzbXo9CVLf7XJqTaTybcabb7qd0eLaEVqyqx4ay25P\nBEJDxd1dCeAtDGHibOnW0hpnn56NBgvChKBwu/prF97m8lV3zblkzM98/ZsAnHvuy/zOv/s3AHz0\nxh+TSRaeUZaxBFUnUYPevtvJry3MUUimzV5/SCFu1mCKni0s5NJJTp864QMkb9+6U7JMJMmEutjb\nuo0wivzs13+JtZWVmdoHZVC1+/FoNCIXmiacEuEcj8fc23Lvc/PObR94GwQBoeyiymDHEpOMnalE\niSmKRytFKoH1W5ub7Ald2G61fSZVrdkgEDdPGEVYSTQIdOADfmdBYSxpSdugvEhmQMJIgv4LZSea\nZtqSS2ZUUI8nQrnGkkiJEptDIRvy0SDzrrv9kaU5cp/rNUs2FC9MljOWjJ3uuGChWWYUOj0tAJNr\nRBeWYpwxsrPrviil/MQSBAHb226nO05TtLi7rLV+t22xPsPNWMOFC46Wv3r1iqcZiqKgTCOL64mf\nOOr1OsORO+dB94A7I3dMksR+pxuGyges15OYuKRup7IFdRD6jN5HQWvFvATGFzr0OVtxoMjE0xXl\nObEElIfakIqe2+b6Dc6fdxo958+/yc6287xEUU4g80KQtDh23HmCvvTiz/n7ysYBdREIHRb7bO1I\nEkqzxty8o+G6fQOekp1kKdeS+mN5UOfn56gl7jkdXZ5j3HXeJcYDliT4uFGr0xJ6qBXUUMJURI2Y\nHenLu4XxJblMbhhKksWgkZCNXaetDzMWhDEIxj3+n9/+dwDcunWdwcDNVVqFWKEO7966w3f/w++5\nc5qQ5z//VQCuXXqfOJrdT6FsgJEEFidKiVxLefHJYpKvgFLWiyzHFH6eaScJcSaMRH+ElfI/1qgH\nL3CHwm5AUeoOBn58G2unhD3xBLTLzJ+5iTTn5wlKkehA+RN15peYkz420C2+/8e/DcCFj88Tiveq\nSFOsmCo6VJ52VFj6kkm6tbfPzuaW3FtBoyUJPo2EVPrAeDRmadE9k9XleQbivc2zrEzAx2Imnq8s\noyhjS2bAn4Jo56NMotlwKCbKlq7ziXkUqMK7sS9e+ZDFRWd0rK6t+iw5BVMqxz+Z6KkLH75God3g\n/MpLf5mGTC6vvv0eWzuiJry6iOpJhk1vn/c/dtlBr751nj/zZ37O3X8ccOeey4IbE2Mjt4Ceq51i\nfyj3H8xxd1folv4mWladG3euU9xzQpkrR08T1lzba+0OtSl+Omm671UYUcyo9FqPY27cdAZUvLlN\ns+nOp9nhgw/c92snP8+v/+3/GoDfa63woz/4twD09zd8fI01MbnMDjqK6Us6dpqmRDKIbFFMOrMO\nfCr/drfLu+9LerIKWFxyiuzd7j6ls1VT0N10C8QP/uB3+Nt/93MztQ9cDNO0EGGZLj/9/cbGBrdv\nXAOgGE9kEgpbHMrMmqayfZ8NtKc7p+U/1LTwo7EUIqtwsL9PX+I92kvzrDRcdkoQhmjp+3YqJmsW\n5GMQe8Up9ZeSDEXg1bN1qLHyfV5Yb7SORgUlO55EIVEZ0xQY0tQd3x+n6EIydgLN9oF7j2c6se+n\neVYQiRFdCzXKjuU5ZES432pVEMviGKuCqDFzE121gKDMTBvQk4xYpdRUmMB0vFJAIbP8pcsXGY8n\n8W6lPEaWZX6OcSrfpZF4OCMvFyFW0hQlGZHpuKCQOpJd6wQ14bDcRRRFMxsb1uIFLUfDEU3J1FMq\npCHj3BhDXrgFdmdnnxvXnODh97/3XS5fcWOo1SiIY8moykfYkTu+0Znj8kU3j+xvfsTnnv0iAEuL\nz7C07GJVjB3z9ruvAbC6tsTP/NxfdPdm6mgRkTUFKFHwL9/9rLh4/TonJBtrealNK3HP/trVu1wS\n9fH23BKdI+5+skxjcPef65z2ijM2b9247TcDJjNe/mHD5Mx33HNLkpav6XawvcHVXWd8h7WETOaq\nQX9ALXC0abc34vd+/w8BOPuFr9JecRvSq9ducEQ2ubPAFgarxJgKNYWM6SSMQDLOMmMwMugUhlCO\nDwY9hjKGsgBs1ym+jxREkuFoUTyIkZt2PmgLtsx0U5OoPTMlsXBY2mPm5gFw4sQRv0Et8sxvkmv1\nhPaye2737u34OrLf+rW/weKCo1yvfPAWH77xI39hX6tDhTREUmdh5Sivv+pCea5cuMDXvuWEnJO4\n7iVmAh0ykr4dqHyqOknm5V0sCjs1/sbZ7HRtRfNVqFChQoUKFSo8Af7UaL7DlJ99nBjvT5UjUN6d\nrCgDaRUZg30XkH37+nlOnngKkBpdkxSGaQ/mTwQ6iTh+yknWR+EKn9x0ukenn3+GExKUWm81Wb/t\nMv6SqM2trguS3N7r89rbHwHw1a+9wj2pu1ZvLTKWci/XbqxTWKF5VIOx6KIERY1B7HZhcTuiNSe6\nUaeexoi132h2sOL1yQqNyd15tE0hn23HqFVKd8/RT8899zLzcp2du1e9JyWzEceedp6g//zvn+Ho\nmacA+Nf//H+le+8aAKPBwFNIBJpu37W1Uat7jZQoCryOktKKfQnOfv3NN7m37XRfnn32BeoSFH7+\n/HkyKYkQJxFB5D6ff/+P+Ef/owtI/G/+5sePbKO11nsK8jz3QprFeMydO26nfuPKVfLU7WL1VFaM\nCyad0gxSn66j6MQjSy2cAlW6qbT2CQhK4Ws5KqW8vs69zU0SKatTr9Uc7QQExmLV7Lt+ZcIpT0pB\nIv1olOc+yH6M9fUbwRCoyedyzGm0D6YvVMFI3ldvlLMskcNBEpGK7lgjjDEiatrvZ9QaJf0TYsUz\nFWhLMZbSE8pSyPPRQUSj+RjZNWFIKuUg9vb2/LOyWnk6b9p7NV33st8/IJcdbb1e9+KoeZH5Gmwq\nCBkNy2dumZfSR3c3eqSyCw9qIVoCol39FxGXzA2FeDvScUYhQdBaa//eH4U8y9nYcJ6Ie7tdinm3\nS282WsRRGeBbZ2vL0R9/8Ae/w7tvvQHA1uYdmq3prM1S4LHwWVSj/iatOdemQe8qb73l5rJ6o8Oi\nlILCNhmNy4zSERcuOm/XkSMveu/YsDeeUFfh7BQfwObONqvPOO9+fWmO/XXX3l6/x+3bLjPrpFEM\nhLZtJQE6lrGVBbQkSzUwQ0LpXzZQXjS5e5CxMu8823aYcldq+XW7PYz07Cw3KBG0bNcC+vJuNw+6\ntEX4+OPzr9K46YLam7WY08dnD7K3jCeBJ0ZTyBgKahFK/DA2y7yXGIv3bA+IKJQbryMb0pVA6mEU\n0ynLEpnigXE0lqlAdmMOhR74uZlJOS14PC27aWgVMN9xbuV6vY7VpQCw5e0fO+/epQsXvd9pb/ce\nYwkQ39/aQEtAf6Em2nFxGLl6s0CrGVNvuucwGPR96EGn0ySTsJHBYOhZhjAIfOB/PpUJHTCZp7Fm\n8sxnwH8SMVNO+fTxzjNdoPiwJeY+a5Vz/ZIb2PlgmzsiSLv8zFeJZLFQ9r56fLPfwkNx+vlXOHXO\nuRhf+5PXuSrZJEdPn/Ku5e7BAUMR57x9fRMrk7bSAVcuOSOrs7jCsbUTAGysr3vRTl3kzEs8VBAZ\nEskEjINVxtJRarWAs09L1gvQl+yjYW9MT4yWJGlSi93kG+Y9RhJLAc88ooUjbCFZNDRY33Dt27u7\nQ5I41/a55z6PlGJD1Rr80q//HQDOvvQS/+6f/WPX7ovvsXPPxRx1u2to7e6l02rT9MKVcFteXLc/\n4P2PXZbkxuYWZ864DJAjR454I+7YseNeuDBKmpw56VKbN25dZPfe7IrEo9HIUy1hGHojYn9nhxtC\nLeTjke870/SaM5MmsUhq6ntf6DoIvMs7UMrXQlNK+bp7Sk1Ul401fjI0ec7munvm7WaTWDKsWrWG\nT4WeBYUNkZA8ggAyU6Y8K3o993mzO/QGRRJHnm5LImi0nEE3yGDQk4zLMGC3544ZDOHoSfdemp2Q\ng6HEP2QjUfl2qc1NmfSCEOSREAaaXGYmQ0iZGKXMpBjrLLDW0pW4vMFg4Is5qyiQmphy3ilB1DLO\nSwcK5FrjKRrXTolOzs3PcyBq/asrawxHZaHVAi2UUk1NUSlao8WY0qH2QTBFXpCWKt/GzvwW0zRn\nV+rr9ftDahIzV0vqDGXxX5iP6Umc0Z/88A8wkoXZacfUG65dSTKmlojIZ1LDGrfJOhj1aYgIa2BB\ny/OIggFZ6jYzRTGCwM0H65ufkHcedwAAIABJREFUsP+am19+9mdi6ifd3DQcDP38HggVOSuywrC7\n4TZvb7z6Fvs77rpH1pY5teyMF5322F53G5stm1IIBdZZWOTMU26eSLQhkMxgHdUpgrICQcK+yDak\nvR3UyH2uRwmmjNdMR6zNuzZ+/nPPc/GGk2XZ2tqgsyDyH8cW+PwXXgDg2pU7nD27OnMbdWAppMi3\nVe5vgLwYeCMO5cuqolEU5ZRTa6ALMdB1QOuEWzOCQvs4IHWfAXQ4XllOaqw3mpQ19xlNDwvNmX3R\nPv/eOxMV+f0ei2uO2qs153jnDbdOp1nGwhFnpG/dvEpeSnfc2yqHIrmyXoIijkJ2t1zfePu1t+l4\nym/RV6RotFo+k7Tf73ux5CjU3si6v3pBiTDQbNzemLmNFc1XoUKFChUqVKjwBPipB6CXuL/K+uOi\ntHJlv+4+W5d1BLC9tcH69YsA1HRKINZ+ECjvunN7wCkP108Ad9Z72HhH7qrO8pILntzbOmB31wWU\nKzWhc9wu1v32yNoaZ59+yh2/v08ix5w6uogOnLt9/e42VpxIKgrZ2XK7pNWFJZ5Zdp6h48eXefEF\nF5A5HOd8ctndz4ULF7hz03m+2nMdTpx0u6eLFz9gc9N5O/gvvv2Z7dMKtncdfbq1d4uFJUd5jDZr\n/Pwv/BoASW3e6xEGOvf6Pi+89LMEf+vvAfDqH/6fjHe6cnxCu+nOE+mAugTbN5sNbt9z37/z9ptc\nX3cU21yjQ1+EOm9xg07HtTvLMtpttzvJjEWcNvz8L/1lLn50/jPbdaiNWntvURzHfjd/9+5dr98z\njem+rLQiEGGhPM8P7XimA4vL48Mw9N8rpQ4d47WHiuJQLbSBZCWN+gNWFtwOL9QB+WP4VseFcsX3\ngHojAgkW3hoZbt+cZL3568Z1QkrPhCUMRQtMa6+po3XAMCvLzETEcVPaNUKLh6OfxgzledZrNV/2\nYZCNKArX9oZTHHTXqgVY7Z55oR9vmA6GA0lKcL/z2XNRSGYmlGhJ81ljpp7gJDw3TceerjXG+ED2\nzvwCLem3cZwwToPJT33mlfJeJyi8hyaOEn9OFUAkOm/m8eId/DmiKCaKnJckLzLS1HlYDg663F13\nYz7SliAuxV8L6qL9tTAXs7TgvMGt5jzZ2J2zU+T0JGA31pqmjMs8t6yuOM28ucVV+pmMxc3LjEdu\nJ3/37mVOHnGemjBQJKJvVatFjzXvHz96lLaER1w5f5lbXefNHhxsMBe6TnLp8g0GQgvnRQ5JKaZ7\nlfffcV6PzCpa4nXcGRyQSk29RrvBnGh1HWzeRku/qFnjS8vkpuCrLzttwm//8je5fesaABcvXOXE\ncUdBHj95hLUjjjF4qhNRS2b3U8RR3Y+z3KQUCM1qU4xQrjZsosQDqNCTkltoaqVwrC1Iy7CPMEIb\n6Y964nGZpvPu9z2VnvPpUkg/qQK2Tx09wo8kQPzazTt0hLEZp7lPrqk3O+Rl2EIQomW+yfoDhvmk\nfJUv9dWOGYn47t0767z0hRcBeOUrXyIdlSLBKUlcZtNqMnmegZokb+T5ZGJRCq8xqZOYW+KFnAV/\najTfT+ycgM+zVBMF2I3btxj33UTa1imZqHanWYrPIFegzKc71pNgPIJ7koXX6w04Jm7LhaUlvxAP\nhwO2tpy7ulGrsS8xSGtrqzxz7lkA3n37Pa5fcTEKf+M3fo3FRbcwvfbj1ylS11HiwLLQOQLA1175\nMs+eO+3a264z/Wq/fM4ZTTdfPMObb74JwPvvv8e1N34MOBG10WhSo+yzYLOYSLnO39+5xOULLsbg\n+S9+i6ee/pJ7BgOLlF4iiJQTUAWGCk6cdbFU2+uvsHXVGTiD3sCl7wKt1hxKxCQPhmM+vugM4hs3\nbvnU3TCI/CJcmDH9gVCgWlPqZT797IssnpKClXGNb/z8t2ZqX3me6QK2JZW5sXHY5evrVJmJMXU4\nNkofygos6aEoiryxlmUZmWSMhGH40N9Oxs4k3T8djX2sW2ot2WOM5lFWEEmRx3SsKQr34417PS+k\nGQQTR75SuY/TyAhIs9JIzH08V5YNsPLuVlsJsUg47I+GExFcHXr1bAwEIskQ5hlZSXVojRUx2tFw\nhBVDLG4WmOHsNN9Br08h1w219s/KWktSOuXVhIzNp6QUonqIlV1OPsh9jA1WY2WRWls7xnBY1gOD\np886Sun2rZtYycKKQk0gNGKeT4xiN5GXhVwzbxS5fjKbsWGKwktyGBX5RSaKQz78yMUG/uj73yEb\nOpovH48ZCf0RhrC84t758mKbTtst1HEYeXqo02oSS3yeSXNqEm9Zb9RYXXNSI0lziaiQFMu6olmT\n4umNNmHoTjQ/N0dSK+v3fTrm9bMwV494+SW3SKaB5pONawD0e3BMNlEKQ03qtRUYejLXj8ZDhplk\nwbY6LIpBlxU5u/J+RoMD9KIzplqdOcZl7VAsiWRwdmoNvvySC5s4utRiLnYb1RfPnCQUAwdlCET2\n5eh8nSydPaX+o5ufoMpY1nzMpUvu3Z09vgYiK9NZWKUj2W1ZlntDYDgcMCplHgZ9cql12WnNEUvq\na1Kv+Xp2SVL3kgPWKkxp0B9K1Cuw5W7GBvctjv/fHCBjFbO95zbPcVKnIZsQFVnimrvPNM0YyJod\nR/Gkn2hbhhpi8klxd1PktJrut/NzbUIZZ6tLS+xIPdhAK19T2eSFv31rrD9PkU/1SQtGlxvXzIvs\nzoKK5qtQoUKFChUqVHgC/CcRgD5Nk8zqvTp8LmdhTgf/riwtca+kTIrCR/3HcTwl2angMYJ2Z8HJ\ntSXu7jtPUysp0Eh1+oM9Qgm2rcUhpcH70ksveQE8bXO2RaixNxoRiDm+ca/H8qrzQP2Fb/85liUr\nYqHVoC07PieIVrYlndpNhDRkV/LcuXOcPe3c87/4zW/yvf/4fQB+q5fz8ScfztS+IFlk1HM03+Xz\nb1NfcDvUl17+Bmnh7sWO0qldBcQljRJYdMO5wk8983XGu847t7xoSUtNH6VJRGzu9bfe4E/ecJ60\nbJQRCm+3unKEmpT9sHlG76AUVDQsSmX4Wq3B2dNfkO+H3Nv8ZKb2gaPnpmu33RRqtCgmwcdBEExc\nw1MaUvdrPU3385LCi+N4onFSFD4YPYoilpcls8hatiVjcdpLZeykuvvO3i5HpfyFThLGdvZsvjTL\niOqSrWQCMlE4DQNNUlIURvtx5jx1pSt8qiK9NZ5O0DqmkM8LTevpv9yE2JGMxSAnFnqpGFkKUfkM\nUD4Y1toIU5ZasZahCGDWahplZt/xj7LUezCVtZhSxVU5AVb/h3f6aVJxbQaBIhLPRB5GSJIUaWYw\noplUq7d45llH8xhjuHbtmjyrmLZkFpkiIyi1z3R4iK4tR6vSarJLLgqCSbGhz0RuCmplTb25RSJd\ninNarl5wXt+3fvQDmrWybxYE4jJOsxG9nnj/Bsp7Kcd6wEDelR2NWVhw42l+YcHXG5xfnMco51ko\nbBtwnp2ovcaCjL9O4yj1ekPupz5VN9JM6iXOgKeOLnFv33mGO8sr1D31aljfdl65/f6YWPpsmo8Z\nS8CxsZa4TITJM1+uaLlZR4mnsT8aM5AkhWI8QInHpzDWh4zMzTXpNERENh0ySsuyJbHXxIuiwGcq\nWhTmMWpInv/kTTZEALhWq7G94ebX3RtXCEREdG+/x+6+m5NGo8x7SgOlyMWzvby84j2rSimOHHes\nyPLR46ytufXjxInTtGR+ZUp/ylqDkfnDGoO25ZqqfKLEYdiJcO8MuHBz3YsBx1FIv+/OPzYaK2Ou\nyFNf7qU3GHkPbVyrEYgnXwe5v+5B74AdSUhYWJhj/ZYkK3V7tCRBpl6P+fB95+nb293j3LNPy/PJ\nKadqo4z31hlrsOLhHQ1TH6Q+C/6TiJlS4Iv+qPs5Wq9d8PCixOXDtXZSO2hx+Qjzy64zde8OSUSh\nUBUWFZXUnplyb6qJC/CzJNkfgXFvk2vnrwGwtX2Hn/3WLwCQK8NUAhH1Wql+rGhJ9tp8p8XWthtU\nL3zhJSJZdXa6Ge995DrKU8fm0CeFEqtNBMmcaq20RAeTBW9Kmc1RCO45HD16jL/6638FgOaRo/zD\nf/g/zdS+peNPc+FtJzNQpAELxx0tWRDTl/iKdjJ5t1FcI4qERgkKRpKm1Zw7w9qJ5wGIsz0Gkgb7\n3T/6I2J5HpevXfUq3a1Gx0+AS4vLLEihzHdff9fLIczNN1lacROFUru889ZvufPXOqhgdrXHer3O\n7p5zE6/fWacnnL7WE+NiOr5JcbjA9oMMK621Vyiv1WqTmD+lfDHh06dPs7TkjM27d+8eMtzKCZMw\n8LENvX6fLaE4W43Q03CzII4TmjUxgoLAT1bdsWU4LuvicWiTMym2rDzNYKfbqTQNoXZOLNfI8zIT\nKXdxCUCgAgKh7WxiyYTOK6wmLWUwxpmXi3ALkztNmhrS0eyTG8FkITC5IZDFLjUFuVCTURiiPBUY\n+ONjAowYJ1ZZ5o65/haENYyexLs984zLfnWq+bKoLc6zte4o+kuffOzH33QVBx0ElPzDdF8KgmDm\nDWVSTzj91DE5ecBYamNqUxCJcTaXxCihn/IigKjk3xWDgbt+v1crw4xQ2o1lAJONaYqyeCdukHal\n8OzBgNJsT7OU9Q3JYjSaZ150wp7J2TmsyFgUTIQPnUDsTM0D4Etn1vjxu04M+O7dGxxIPc9aGBCL\nAbizv0+cuL6mtUZCrLDG+Ay4tCi8+kuhwEqMVRzEIJmX3f0d6mLijpgUcx5nkY+xsyogkFjAophI\nbFhjGAvli8XT3bNg3hoQ4/7KpYv0hQ67dOOWjwdVKuT2ulsbPv+FL/PSS26juLq05g2BpaUlH/9X\nFDmtBRdnq+LYU121WsNnw1lrvMGolfGVG5TSk/AXZZmuPTLRuLZegHkWdM0VWseF4h5k7N91dF6e\naxRu/lbaeop51Bv4zcYXn33eG1aXP/4QI2M3DFt+zPV6B34tNCZD7DaGwyHrd+7KtTKefd6FftTr\ndcZy0KWL1/xYrNcj9rtCEw8nDo1ZUNF8FSpUqFChQoUKT4A/Nc8UTKi6cg/qPt+3bRFL+LN23WWt\nJOeEkfNEDY6ecYGLm1vrHJHyB0kQMZ7S7ykd6sqqKcLv/ovNHp6+u7PO1QuvAnDpwnmOHXU02FPP\nfgHDRHxwoVOWfsgZ9pyVPuju+R1/Iwon9ZGMYvfA7Qq3P1nn42vO0j662OKsZOQ9dWKVpXm3Y2rE\nEeGkSuEhL1v5Kc8L+qn7q73yLPX2iZna97Wvf40bnziRNZotQhHmO//mj1hbceVb2p1FVo648x09\neZJAaElrQp/lZ3SNeEm8Wvc+Iozdbmx/d5Mr77hA7xzLQtPRBivz87RaZXaYpd1x37c6TTbvut8G\nkUYF7vnt7K6zu+92Hude+CannvryTO0DCJIQq8sAz66v9eUeadkh7XR09qFSIofONVUyJElEbDUI\n6EsyQi1JOHLkiD++pIr29/e9ZtZ4PJ5owOQFkdAVmSnYESHTpNN+rNp8zTpI+SrqEWjx1t7dtxQS\nfGq1IrBlOZkRSgJatY5cMCcyhsotvDK+Xy/ONbgt9OswzyaPLQ3ASkB5gnepKwX1SLKVVIaVLarS\nlkAyr+7t9yiGs4/FNM8PaeyEXjBSYSUbyuqAksMzeUEipTnqaO+B2h11ndcK+Kt//a9z4rSj9mqN\nGpFkuBVFznPPOS/V+u3b/PZv/isAklrN94GiKFwgPK7rlCKiWZZ5716SJP77RyGKQmo1SdboDWnU\n3fNrJjHLokW3vLTEeOS8l91BDyX9em6uQ0NKs9giJBTPbRAqT2006i0aiRvf/W7K5h3nrc3MiEbL\n0dE6MJx/31Ho12+s89HHbm76c3+hTvMVJ+irdUpLhE4LYx4rhvnc00e9oOj5W1tcues8U1v7B9Sl\n5mBGgRG6OFSx96qYfBIaMM4LUtFsUlFALkk8JCFZmbWila9zl2tF6SYxyhLKtXQY0JAge8uEns2y\njDwvKULzWMTG3/krv04q5/nBD77PvduSWf01xZLoMSkd8sFHrr7skaMnmV90z78o7KSkUDrw4Qlx\nHGOkhmRqCjKhC1dX1kgS0fpSCitzWz4eUFIncdKY8qaWWe+IqK2s1sbNw7MirO2xcNT9dtzLfXJH\nSINOw42n2zfv+pANq1xGH8DK2hFMXnoeFWk+0b4rk7rGo5EX5AyCgLpyc0YURZx71nmjwgCGoimm\nFKQjd55333rfJ9F8+Sufpyd06sbGPY6fmMzNj2zjzEf+BPCw2CirHj6+Jl7Fh7+4Q5lOZZFZo1k6\n9hQAnaXjLC67h6J0eKheWikg6M4xJZNwSBR0tvYBnDy2ipEMpbVjx7h65SoAq8fP+GLFmoDCuk4Q\nKrCqfA0B5ZPIxxlFmY6tM2SdIQgbjDM3sK+tj7kuac8LH9/hxBE3yM+dOsqxZRfH0GlGxPHE2BxL\nFtbOTo/zV53b+AdvXmJh5amZ2nfu6Rdpt90kGdeX+MY3/zwAGzdvceemG+ydxSP0x2VdxIssL7uJ\ndHXtNK05Z/wV2Zher6yZ1GFcuEl4cXmJ3T3XmQsyFpbctTqNBnUR/AtqDa5fvwbA+t3bBLJAhLGm\nK5TcxsYO43Epb9Ch3liYqX0A4zTz7nKM8QuysUwoiqmKpJbDdNiDYgODIPA0mbXWL5hzc3PeyLp2\n/bqfHBxtVFbfNEj4DkkS+zTz3mBIIROgfkj9rYchaYQUunTlK7RMqs0kJgok89WEBKKi3Iq0U24E\nULk3NAqjsSKTUKSatUX3/cgWHIwkVd/kvradUhFjmQytMr7wb5AEaKGdcmspZIOURJZQBCXzLCaK\nZ5+yrJpK87aGQlz5OgzQcVlIOfcLn1L4ichlhhb+857UaRsM+hw96jZmKlAE8hzGaUYs50xqMXEZ\nkzUdzqCU7wNG4bM4p+UwHifj2RrjKejxOKMlm5ZGrcHp0y42pN7qYJR7P80go5BYmFqsaImAYRhE\nhFJvrlaLMaEsyJHmzrqL4VxsrXD06PPSjiaZqEbevL3JnVtu87O/16O47uajO3fWeeWVku4OSMV4\nCaLDVPmjEHSanGg85dpYn4cfvuuu1e+TiiFpo9APTKOMr+fZqiUsygZse3ubYfk+w8A/+yis+dpz\njUaLTGQvClwdUoDPvfgCy3PuPGbUw+pSxmJiEJsix8qCbAvjswJnwYnV4wzFWPj2L38bK/GjOlAE\ndZEUGY557ln3/G/d3qTbc+8o14WXu7BhjuwliWoaqyWjNFA+Xqzf2+Zg3z3/OJlQ1spkFCN3fL1h\nCCSuUUehL8KMtT6+01jrN0KzQOsQJbINNBWrUh1B5ZrRVhm7l/qafdYWnrY7/+47RPKuT599mlvS\nx4wpSKQIdpYW3BVx10F/wLKsf8uri5x9xsUJb25s0e06Yw2luXLRUfFHjqzQl6zJq1duc+qkCw/a\n3t7xQr8ztXHmIytUqFChQoUKFSp8Cj91mu9BOy97X3D5g3Ysn+2ZKoNhNVZoCUNIIOVSVk89x/Wb\nznXaWPochKVHwWJ8psJneaZmt8CX5lqcOOEs4Z1Bxs6u2zneXr/Dwkrpsh35DD4VaqwuMyfCiacs\n0N71iClQosFTDA4mu/bOEpnskm5t97gl2lUfXr7DyaPOA3T25ApHxTM0Tg2XrzgRsg8++oTrm45e\n7PYNJ06enql9rfYxmi137oPRkGPHHFXX6464t+30bL5w9hzNOUcz3Lixzu5d5+kYHtwmrLld7EH3\nJpcvOBG3V770Mus3nCDn62+/T//AHV+QciCUw4kja8T1UgvH/S9AEscoNQl03t12HoQ8dZpVAJ3O\nPIGevavHSY2GeHwaSYODHXdOdICVNEyrJh7L4L6g82mqr9STqtfrnsqxxvjvi6LwWXvTdI+aysSw\n1hAJRbW0MEdbsnFub9z1Gl5JkvhzzgIV1jHiyto6GBGVyrEmoFlqeI00zbr7fGotIhMKZK87pigD\n4vOQVLw/nUSxMu+8hxsHBWPZbc81InLZxebWUgTl+AsxmXgn8xGRXDdNCwIJlA5CRSQUS6uRkDxG\nORmD9QKqzulUeoggKIPmo8hTui4fVjxNWAZ5SQuFPlNrZ2cHK94drSJPodfrie8PeV4wHLg+rKd0\nx1xiC3IP6pBYa/ne7+8/nwmL96oUWeap1/E4ZXnFeeKPnjjNJ5+4/tVsNSiE6rJZSrvhxseRtTXm\nmm4n3+0dUMi7GmYZypSZi6scWXXerq17e1y74Xb1b7/9IVnm7uEXf/GXPRtw7tyzaBlzxrjSNwDN\npD5z7UGAbv+AULnnNBqNfdBwf5SSy7yf5TmhRNAHWhPJGvDNr3yJL77kqNf33nufW3ed5+L2ndu0\nExmvjZhcysysLsyzL89nuLdLa85Rac+ee9oHOo+GBUaVSRATLxvWov36ga/nOQuUDjiQMl/9Yuzf\naVNFk0SVUJMIhZ7dGdMbSRmhLKWMlC/SzMeEnz19hrGM1/1Bn4ZkVu5t7bAr60QYRxSy9nTqTULJ\nUi30Nl3p+71ejyXRizu1PE+j4e6hmxnubXdnbmM6VJPsMKP9mBgfjLl1wTEk6ShH2HfCQJNK22/f\nvkooWajq1FMTrbYs93Pe3k6fextu/VFKcVPGX6vd8N7gQX/E7o57bqury2xsuOu+/PLnGIhn6uLF\nq14Trdlq+uSgWfCnKtr5MDfvg9zA9yf5HT5e/p1a4FAhqby8leNn2Vh3NNJgMKbWqZW/nMRYWZhE\nTU0MK2vNY9F8tVqNl192hY7/8EevMzhwC/FH77zKqafdwG7PLzKWTtmqJcSx+xyFNSgnICJ/D1rl\n3LpyBYD3Xv8j+kPXUZZPnONzX/kGAItHniLPXIc7GBa8/aE7/p233iZIXQdqtJfojdw5P7pwxasA\nnzvzNMyYcp7UllheccbU3ctvkhbuXl544Yteffzo8TOMxPhbWLJsCxXZ7DSoSexEVvTRgWv3xYvv\ncefGe+57QlZPuli3ztKyr7u3cfcmNncxGyeSxBfFffrsGayV2JxR12dMRmGDhXln0M3NLRDH5Tt/\nNEZp5rNKVpfXiMWB2x8P6UrW4TCbuPFje3gYTfff8nO9Xp8YXEHgqb3hcOjjENrtto+TGg6H3qVu\nratRBtDtDYgk5qE9N0dn1VFOzWZz5vYBRGbkx41JUzJVxvVoOpJpOkoLGlIotl9ocln851YCisI9\nhzwdEYpy+bmjdYJQ2pX2iEtBQJP72CuTWuoNcc0bixKKO1GaukyMUTDC5FKwWsUo4RySEBqPMWMV\n1lAyC3GS+BqYWEMk7zSII1/M2YAvfloUhc9iSuKEhQW3sF67co3rV50hcebcWa/uHwSasdAz63c2\n6O6X4oPRYaVpmZN0NKF9x+Pxodi6++U1HgaNoib9qN8b+LRyk+ckIoT43Etf4vaGi2Us0n1fbzBE\n+Vp+oQ45cdLFlbz99rtkk6qT1CVmajDUXL3uFuFavcnLX/0mAEdOPkshVPCLn/sC/XG5+Cd+gQrD\nwMskZFnhhVSn6c2HYS6q++dXZNkkzizNvRJMYEFC/ogKw4kVFxrwrVde5NxTbq564fQSewfut7du\n3SIWauzO/og/+pGTX+nvbPLVl9zmMB8c8NHHLqX+9q1bvHDGbYRTY9BMxmU5dhV4WqrIDcFjBIZ9\n8v4b9MuM5NMnuH3PGX2XP/yIUS7lLpKEe1Ix4pPzl/lERFm1zlhacX3z9u11mrKBfPGlz3NJ1ozC\nWo6LTML6+jr7+25NOnrsOHduu811lMQsrLr43lq9RiZ9qTvOieRBf+7EEc6dOQPAtc1dtrqzCT0D\nDPcytDgBQjSR9PG2qbNQd/N3z05kHjJboGWCyowlk/Xp2uXLzLXd/NdszdGXuqDd/5e9N4u1JLuu\nxNYZYrjzfXO+nLOyqrLmYpFFUkUNbDSlZluSJbXa6PYADzC6v/RhwPCfAQP2lz9s/xmGAX81/CHD\nkqGGpEY33JSopkiJQxWLxcoaszJfjm9+705xYzqDP86OEzdLJPNmZ6MoGLEIkI8348aNc+IM++y1\n99qTKbbPund07tw27pBy+Z2dB4hIRmcynnr6dXw68TFf0+kMs8T1f7vdwgOqx7e+sY6XXnp56TY2\nNF+DBg0aNGjQoMET4Ocq2ukF7NhPvoYu9P+7aOs/VOV5Ua/K1+ljsJSrF8Z9PP2c0+WI2z1/Il/0\ndllYrzWBh4KIH0eaDPjeWz/CD37ogiR3795HNnHelMO7E+zfc/pMw9UNbG47fZjN7bMYEiXWirvg\nlB1korbPVtG6xDtvfgcA8Mn1N72Y4N6dmziioO+/9zv/BTpD0pyxBiVV3M7Hpzi+60qyrG8/BdFy\ngdiWxThLFcZbkuO9t79PLfgvf2b7bNDGmbOOEnzvvW/j5NCd0l957WuYU1aiVQYicF4qzhJUMXyj\n6S7WYuede/rpN3CN6hD+8e//j7jxibvPV3/zP8RX3vhN1wcqAifa80dvfhM//uE3AACzufGZGEky\nw8qqO2FbAV8x3rAAZ865U9SZC0+jwOBntmsRcbvjKa2zm9u4uOlOt/Myx919R0fe3d/FjIJA9ULt\nvEVv6iJlI6WshTe1RkQaYqN85OknqY2/ZtFDoQEY8iJqHkBz9/fW+XPoDh09U3m6loU21tPINjSY\nztyJ0CoJTVGs7ZZAQIKPmZFIqWabFRoZlZ8ZDiz6MXmdWIBxlaUYAEle/ZYGp0BRpQLEdH+VabTa\nldAkUJrac9ernBbG4mROJT5EieO/WRrxp4Jz6WlQCFlr6mjty30YaxDFMV3PfaYksxYRJYxoBTxF\nJ3LBAtyhsdpudRHRKTmOIxzvudJKd66/D0klPiLLMas8GaEAp+cJmECVS2yY9J+TBOxS7dNK+aw0\nyWs6UavSa9Rde/5V3CZK7sHt95FPqcwGFPZ33fOur65ibXWD+iBCLN2Y6nTbeO+6W7OKM3380i/+\nAgBg+9wFbFIGal6WSMlmxSN5AAAgAElEQVQjF4Yxjo7dGnB4dArOqpqH8KKwrSB8rLCJtghhyINV\nlhqF9y4ycAo0/9xLL/i6izs3PsZrRO09e2kbkjSMwpijHbh3dX71eVTZGs+xGIcHrh++/+Zb+LVf\n/hIA4MxKH//HP/s/AQDf+9738OqLLvj7/OYQoLqHVisYX5OToxK1CoSsk0eWwL/6o/8be0eOovrl\n3/4dBB3nWbt9dwdJ4byBV555ASsDCqoebuDd5F0AQCcU6IBYDtFBRlnfd2/ewYzKmoVS4ti4dev0\n5Ahr5IF68fnnsX3GebVUbKBbri2lUuB0n47iiCi04TA9gb5TCfFyMLk85d5RIdrkYW4FITo0tyIZ\nYjVw7fro5l3sHlDyi1I+FMbmBiUxASIIQE5rTEcce4fOyyal8JTfvfv30es5BmR//wgZefqeunIW\nZ887T9O9O7vY33Peq5OTEYpC+7ZXmZvrG6tYW1s+cenna0z5z4FHcWk/XbKzLg7q4iIW0tgrwT4L\nDFadC1CrwNf90rBe3JV/6vaVy25RbmEZfPNb38atTz6h57HQVCcqFBrZ1L3U3dnIFx/t9FdwZtsZ\nNVub21gjpfNwsA4Tupc6n4ywv+tEOwMe+xRyaRn2b+4AAN7+y2/gK19zRogVoY+BsVELw00Xw5Vp\nDpO6Re3y1WcQd9zgO9p5H2VytFT7WBBiZdVRS72wi/17LluxfBU4OnT3OB7PcOE5R3XCFj4JzOoS\nd6ken1AzzMauCOn777+Np55xi9jFZ34F7990lOyLzz6LNsV1XHnhcz5r7ODeJwjhNqunrm5g/8g9\nQzoqoKp0Zqu8cJ4RsTfuloGwgKhqiTEBSUZ2JHtoUx2v1e1t7FLh5ZO9fUwpq4ot1FZjgDfQiyL3\nBg9jHJUmhzILxZDLmqZmjCMgwTgRSvTX3AJ49vw5DGlRXV0ZesNaBvKx1HqPxhxx2313lgGzifvu\nSldhMnbPubJeoLLRrAnBNRUcVtrXXRusSnDrFsbTTGEQuMW2F1gE1K6VNkcWun6YpSUqLU/BOTgZ\nvzKYQxl3AJhP216dHSZDTllG7Y70cSDLgHPpYyKVMXWxYmt9rUBjS1/TjnHu47akkJA0fqQMkFFx\n6cuXnoJKqcbmZApOytjTkwlOaI5O797HOtHpM8FQVMrSApCs+i2G6pQRch9tAwPr16dHQSuFU6qY\n0Gp3MU+o/5RCTLTv6toWvvarvwEAGB+8gu//G3cgOdjbqSIKEEWRzyLtdfqYzN07WV/dxvPPuXf7\n9DOv4doLbiNq9/qe3mSBQItoZ60tWkTj93LlM2I5Y54+lZLXmbJLQMQtGKIRD0cJ5qoqhMvwa192\nAqG//qtfxf/zL/7cfR5yvPKyM3x63R50SXPOAgF1q1Il/EyxBtnMGR1ffPUZnN9wz9+PQvyjf+DW\n0z/4oz/Bt//Cyd38g9/49xDSIUcVxhfmZQx1xqIMHqoS8ChcffoF9PtuzTvcO8BTL7pD8W/81m9D\nCHfPtc1tHB44w2ElGKBN2ZQmkl4S47nx2BUtBBBqDb2yRm1nKOk5s1Yf7Z4bGxuzHBdojbG2xOnI\nxW3NGEewSsZyJGtl9HmGOKgkB0Kox0gfvtDdgiBjSoS1pEjcirGy7cbP8OwV/Pi6oy/fefvHsHSI\n6rQiRBS3pa1BSVUWjkdzzFJn2G5vb6FaPEenE5+J++rnXvMZ9UVW+IxIKWLsPqA9NZTgJMOQjzKc\nO+f2t+FggNlk+biwhuZr0KBBgwYNGjR4Anz2nqmFzJbFPLqf5Jl6iNb7Kd4ha61PEmAM4NWJCfUp\nX1nrKQQG7rNxFkXHHhIgY6gzDxaz/JaAEMKfvIIg8BXvS1WAV5okxvhAyunoGMnYnYzu3frI1+Bb\nP38F5845j9U8SZDQybjd7mCw7rxss6xAqdyJ5qP338KLX/gCAKB/5ilElP0QxG2EfarfJiQknSzy\nLMeDm44iZPkhXnj+4nINtAy9wRbdL8CNG8679OV5giGdhJIsRZo6i/54dB/7u85Tx5lAGLr3cP/W\nLXz47l+6/uAhfusf/hMAwNaFL+GjD10w+nsfvo0gdCfjUBq0e1SRfuUMDslFq3TmtX4YAqytUdkP\nHmP/gaM3Tg7u4/xTy4uvhVphRm7lxBp/4ujKNgTRJytrG+hQVl165qz3Ru492K1LJDLl+zubzxBQ\noLOMIwQ0LlqdGJOJOxEGtvZGwXLEpKs1WN/A8Kw7rXaGA6xQmYhOECKkH1Nao3wcjSIkXuw2mZTo\n90irhhnI0HkpzmzGKEqqbVfOEFOqDc8sTFXmpyuR51SfzFjvxWPGeC+PYXU5kV4rQMCoXI0BdEGa\nNzEH5WRAmACMdNhEZNEjWjMUFpud5emTtZUh5pTVo7VaqG9ofBmQgEuUpHvFBWBtVScx9HNUW4sj\nyrg8d/4CQjrF3r9/G2zisoR39+6jTV6zPJugQ86XDiRaoCBxGGT0DMemwJj8I4LBB8RzY33g7aNg\nrMVsRl6y3MBS0PBKrw9G97PK4KlLjqIMLl1AQDTZn/+/f4xCO0/Hwe4JbndIc2djG6OPXFt3bnyC\nlz/nxG6fe+lFZJREMD44Qps0qkQQ+GSNIAjQos+NXUFOoQbtTgd1Ms1y+lIViqL0Are7Dx5A07x8\n+bmr+I9+9++7tk8muHPLBVs/c/kKrpxzIpBlViIjr6bSyq/iUgpP/45GY0wpWeC1l38BjMQ/p+MZ\nNtecN/vv/sob+PNvuDqmu3v3sEneq1KV3jO1WGsR2mDJHAIAwJd+9dcwIE/WZD5DKN1EOLO57ku2\nlEojFM4Lc6BKXKLaj2k+R3Tg9o/B0TEsBZRnD+7jhOjXE8YRUZ8PtIWkv1MAGf0tmanDbQYrGHzx\nKwCA4RtfRkJ0G8xCo8Tj8DXA2oWn/dopZIiQvJmccd+H5zZipLQevPOj9zBL3bvOCg0iafD8iy9i\n7ax7v7du3sI9orADWVOrQgq/fedFiTyrPLYp3v7hDwAARW5hKFtpe3sThtp2/94DHwIw7A+913oZ\nfKbG1KKB86l/+Ilw7N9PzeHz/1NRdczaWq114UrOOHQ1460CZ1W8FfN1ihh9H6hoQ+b/fow9Cl9+\n/Qt+ETk4OACjWBfOpc+2YcZCVupqWnvlVmM07t3dAeB439ukYtzv91ERxe3hEOcvuZileV7ixpzk\nDUYnuH/XuTNbq2dr+QQAXWqvKhWOd91CeffePcxpsF4820anvVy2GxdAh4Q351mG9MAN5uOTeyiV\na7fSpU9hljLCypYzCgXvoxU4w+GDt/8I1992Nbe+9Hf/E6xtu2wixS1efNm577PJGVy//hYA4PqP\n34TOHKXRiwLEgeu/o5MHPgOuyC0mFKMWyxDDjmv3/s676HerbLenHtnGQErEjNTKWR22J6PI00Ou\nbe7dbm6dw6Dv4hzek9dx984O9UPhs4yU0j7GKlTKp7FHQQSBmiKUvu5bgPNkTG+cvQBLKbqiFfnv\nzrIZQhrAeZ4jt8vTfGtdA0218CKusL7u7n/3XoFBn4RGWY6q23qG+QylOY9Q6gVDgIzEQnDszyoq\n0KAk2QM7K6FpU+iEHL0+KTanGtOMFMdDjkGlUG5mEBSnoSAwJ5c9GAMzj0GfXLros3dmsxnmpA49\nL0rkPvNKoVCVuCH3wptgdWZfEEpfUPjyU5d81YS/+Dd/hsRUWZ0abdoQOTK0iUINZjk2aYEqwgiH\ngeu3xChkVbssIIJKBFVh2eJ1Smsv5bDSH0LyykAMkJFo7mx8AGkdRdzvtXHpytMAgFc+9zo++NDN\nv263DUnq9q12D+sUErG3fxPp3B2KDAxyopHzPEclTTIYDH2sEDOmlqlhBt0+DR6roamPheg9FBf4\nKDALJJR9drL7AH3aVb/0yjWsdt07+eFHe7DUl9cuXgajcXc0PsYBFcJlgYQkoyCSAhEdco6PjrA6\noALOnS5GJO8iuIEhuYLVfoxLF10f7tz7GHHXxWQxVmckKlhIyhAUViDPlg/u2/neXyKig8fh8RFU\nXskSjGDpICJ4iJwkKEoZQay4539q8wLUD9yBVn5yF+tdOvysrGHvxD3/J0JD0ngPYoGSxklrlqM/\ndAfCqNPB/SO3vsIC6ojU7k8mECRSmwcBmNcRVjCfjo35GYh6a/69p2mGjMJfOJcwFVUqY6yQpMeZ\ns2dx57bbW7QFelSL9dqrr/tDezbP8eCuy1RlWBQRZT4L/KP33sWcqEDOOd6jLMg4rrOfb9+6gy3K\nUL94dgOSxvbk9Ahxp4dl0dB8DRo0aNCgQYMGT4DP1jO1ULKF0X/oX36iaOdDAes/7Z7uSvrvRcqP\nYZFHrKxGzkqfIcaN8J4vDQ3jMwEXfteaBWHPR+P8+bP4+q99DQDw5ptv4oNPnLfIlM69CbgTXFWp\nPogjX/KiLMu6xpsqMT50Ac6zk70644uv+lII7XYbLdLQGJsS+/edlX7h6kswthKXBI6nznv13vXr\nGBOltLKyiiJzHpG3vncP4/NbyzVQAb2eOxlsn7+CEVVxP3ywg4S0OqazKZ4num1t6xI2tt3prSND\nHD5w1N4nt95Hf4Wy7c68goN9EhZcNV7/qh0OcPmCCxicHd/GjfedK3+alWhRgPJkluCYMmFUznF+\nm4QFjx5AGfc8Ox+/hQllMeGf/vojm8ilRItouLjThiaPYpIkXvup1W7VdQYNQ580rV77/BcRkabV\n7Tsfoqhq2AF1WDHj3rFqjEVQUbKhQJa7E21bSHQpULTf7yKtNJs4MJ+RJ6tUKBdI8lwvpxUGAINO\niGlKpUU6EZLM9WduLLq8cnkrDAak/RTm6FBmX1IooMrUQgSDktoSwBBl1g4NFNGCuZLIKFsmTTMM\n6fSMkqOsqMayREBZOlmRQ1DWQpJqFBSAHEJAiuWza7qtwGvSbG+soqB5M57NMSattul0hjlR6Eop\nnzBQ6txTZWAWeeney4Pd+55qOh2fYEKeG8YMMvIuDNsdJLIqc5GjQyynjAJEVB7kcsl86YzTMkVK\nFOEUFloud8ZljKFDAd+tdguCPND5PPdCsBzOKwe4TK7tLTcvX3rtiwCVmbr23NNYpVpvd28/QNBy\nHgoRdDClWmnHh4cQMWUfKu1p8F6vvyBWqnBEHg0mArQHjiYr8sKvqdaaxyqZw8GQTd2axVTuNaQ2\nBgPklEwzWNlCjzzD7TjCdObeyTvXr+M+JYkEcYQ2uVlNmWOL+uF0MsfawI2pYbsLgSoEhANVQoc2\nWKesrjv37+LKRafZJLhARoyHlBI8cvNewy4vvArgT//8L7B99TIAIOcWouPGrBkO0I3c+91Y3cBT\nF901CCPcvOPW+nMrG+iuOW/ObZVhQALDamWGOHBjcP008eNXG4EyquoMWnQp/CVLEpcUAWCl28LR\n1GU4Hv/ohzjbdV7LotsB01V5Jo3HcBJjdDJZ0IMEvIuLFZ5Ky0uNkDJrz1+44D1TjAEbpKdXlNZr\nRuZ5XlP3xuDCRUf/TWcJpiO35wXCYNB1/Sml9PVLwzBAL3bvNJYcnDyn2+tDRBTQP59O8Dh87c+t\nNt+nP/93df+ffM+FZGO7IKvwqf9eFD2vRTsfj+ZTRY4+pWX+8i/9IlbX3SL1o3ffxQll3kRBiDYt\ngsZYZLSwM8a8MjMXwscCGWM8vZTnOcY0UGRcZ4gJGJweuvgNnc0QkBCoUhZTovOsCLB90cVGjY4O\n8fG7jj/WxQSH93aWat/4dIIsc/dr9VdxeuQmXT6bQLadQTG9/wnuf+TuvX7ldaxTXcROOMU33/qX\nAIDTkxF+67f/KQCgP4hx76aTk3i29QoKUEq6UEiqLEOTehXzLM8xHbu+zJITTBP3dzvcxCqleM+L\nCUpSNj45vg9tlx/qXIqaaVmQPYjj2E9eIYRfTLS2mFJNr0AIPP/S8wCA3rCFKWWDpGnqaSPGmE/j\njcIIm9skh6FLT1lKEUKQ8GJpDFRlKFmBkIwUKUIwMl4KXT6WAnoUMzDuFo1Rorx45tpKCElUVBxK\nJBRzBMspyRzIC45+m5ST+zOMxyS8KSOs9ipDOIclxcwkV7BTiquCQkD9sNITPkZJMYO8IEMsYuiS\n8vqw24KuDld56qUvlgGzCoyMFClCxERl93ptnKH3m+e5Lw47GU+8MZsWJXLKzipUjoyol29/51t+\nPChVeLVqZmvRxtxoTKq4i3YAlVUGpgQjQ6mjAUnxg61AYgSiGg1HapfbiLUx2H3g5vze3j4uUBbS\nxtqGzyYLBENEm+p0rJFSO1qDNbz+C3/HXb+56kUa+f4EisbFsy9+HgFlbY5PTtFfccZR3Or64sBp\nmteK1Fr7mAsOBklUrWbcrxlzwdF9DFXp6WQKRX354rVnMSbD2miBG7coA240A+gA8/GtjxFHri2j\nbI5tkn/ptjteed3oAjFl6x4cjrx4qSmVz7zMitwXwOacIaYMag7mKyLEUeSpylZH+pp6c6WRF8sf\nbL76n/7nWO1TtYa4DdABbH1tE60O1UyMQgii0+/cvIlDojXLNEO47dbXq//+38fx/+bkHMKjCeIO\nzctUo0fzOyuVj6fttNpoUdZpDoOY0/pRcKyuuYzhImqjJJosswamMhmMgX0MY+rw6MDHIHb7Q3S7\nbixpoz39LmSInA48pyfHCEVtK9zfcQfpyekIktY5o5WXFDk9nWBjg0RihUSXavkNzmz4OA0phM+0\n5lxAkbRGpxNDEl2bpXOAxkCZF6468pJoaL4GDRo0aNCgQYMnwM+1nMxn9RsM8KcSa0xdRgOo6RbU\n/iuLuuyDNk77ZVnoMvdUEOccrzzvghXPba3gDrlmb9++jcnMnfOzvM5wFAt1vDTj/hmYsD54PZkn\naFGAOytLTMjzwWExJi/RB+/+CMMVKg0QMDDjrP1BkOHuR07A8+6dnVoDK4APIn0U7j64i1hW9FAL\nJVFpaT7DOSoDc3JvgA/eciKjawcPcPVFpzm1p07w3e86j9X22Vdx9qKj8A4O7uCTm9cBAEV+im7L\nnYp63Q52bv0VAOD+3V1srLlSDysbPdy4RZmA5RSMKt6XbAX3DqncS6bQpRM5pMHx/t5S7QOAvCx8\nTTdYwFZB4YGApZOxUgqaPAh5mUFZqvSOAJJOM5euXPGBkEVR1FpCrC5DEQShF4pUpaozUkSIiDJe\nwBnaYVU3CxB0BmJKQ5NnR0CAi7/p9f1p4MKiQ0kQG70CvSG9/0LiaOSeYTiQKOl5UhYhI0ogRImN\nNgU7sxkMaYFF8RxSVpl7Ah2iPYTNUFBZmvW1FkKiHIYBB0h/qlAhuqQ9w2WJkESBkpyB9ESxstZD\nmdVlfB4FCYuKMRPMgFdZvDCIqD/7rTY2hs5Tkq/1vB7PpDCYJu45p9MEUyqd4ajsyvsioIqqhmNY\nF2GRESKiqotMe4EjnZcoiVqdGIWQTs8hF+hp1/ZeKVGo5eaiWahTGEbRQwKxfaLYAimQEM2vTR1E\n3o666PYcNWaYQF66edPqreDKtZcAAOuDCKMjp8UDFmBEwdzDtQDttqPMVGmQzBP/PD3SQMuKHBl5\nxFWe+4BswayvcyeXOPXvjWaYUzLIlWsvY//UrXfvffQJ/pqyoJU26BKV0x6sYIe8dWtrQ2yecetg\nt9X286bIM+REUxZZigGVwUrSFJqezcKgRV7/VquFIXlZBTgMeaM6vb7XJwyZ9BngPAz9HrAM1kQA\nQSESxwdHaJGX7XsfvI+XX/8cAODp566hIE/K3cO7EFRflvMSd/ddBh+mM8RvuLV29ubbiPfdftDn\nIU5J4zCNIjygjEWTZljrDekpJCbkITJlBkXsR6ENDGmpZee3IVturreCyGfELoNur+8z0mUgoegZ\n8iLHhEJFuJQoKHFidHyMNnkPlTUoaK+ajA+915UzjpDGUFHkuE+JPyuDLrr0XSnDBUHlhXAhq8Ep\nlEAEdXZn2GohoH2jzPOHvGOPwt+KQsePkyr7k1Dz8dZzdZ+u61cpHzxM/llXzw/4VG1j+3CVvsew\nAUtjEVRq1aVGKNzg+Mrnn8Xrrznj4Zvf+ja+/9Z7AIBZVtc3WhSzswuBXtZw0B6OUimcHDrDIGq3\nMZ87ekkzYJa4CfCDv/oGIspWaAUCnDbc+XxOmThOoFD631s+zVUZjjB2EzAMe0jpftPJCVZWXNzV\n6voV7JFxdLz/MTpE/ezeexc0h/D6l76O3jql+69cwOa2i58an36MnY+/BwDIkgmSxG0+qxtnsEUp\nz0V+imHLta8Mh7hwydFqr7/xj2GJ2vuLb/wz5BNHAwhbG0HLICsL3zel5p5W08qAUayQscxvZgEk\nIsoUakVxbYgrAwRVLTQBQUaTbIXeOA7AwWVVTJZ5Okkr7d3oQRz6eJh5MsecRCNjKT2lz2ChHkPQ\nMkeAwLoFam2gEFdFhjVDl2Kj4ijw6vWBYhhSzMw4VSjg2lJMBghCMtb7LUzI8uGMwVI8SbuV4+rQ\nvS9tGeY0Bm5PORJqi8kLzKnoaq8rMKbnTAqDKUmpmwFHpxMu3UYwC1FRolLUteCYhaBJLVFn7gat\nGIKEOgci9BUUirzAZOrm6Xg0wXjmNoXJJEE+pxp/gvusT20YdJXJyAOAFuciKdCiJXekZlCiUkkX\nXoSxrTmkWW49jOPYy2ccHR0jJAN9a+sMtK7oaOZjhfJs7g8AxjJkGWWURhyoaiTGLaxRav7mWg8l\nvZ9SGQzXHI0ft3s+NlUIgS5RUfN56oVjBRM42HPzjxkFWcmCCOlrEi6DNz+8genEGXqDft8LuJZ5\n4uPPekLiDGVjPX/tOaSJ25zLbIqURCzLNIWtpC50HTbRiSOUhWtjks79fOLMICurRsKn0QdS4nTq\nDLq1tXVvcMVM+gN7YRRMuHxFgus/fssbdOPRyGca3rx9B+tnHXV7/spV5CRH8sMff4gf/8DVE1zp\nRWhRbOX62gaOuGtv7+Iq9nZdzJEcxDBTEgBuRcgpg+94doBk0/0tmMXhfRL87HLsUmWL4liCT50R\nvXs9xLln3Vr78quvgYvlia2iKMFRFWTWyMiZkM6nKCjuMM0y7xwIAwZGBrI1dahFt9fH2npVMSRA\nTvGA8+nYh2YIKX02MAOgq0OUBdoUY2wBX/PTWu5jQHv9rhfr5UPhJRyWQUPzNWjQoEGDBg0aPAE+\n22y+hXp8n/ZQVZ6kfxsv1eKtftL3mcVDelIP0XYV/beQUWjAfbCwZeyxPFPWcJTaWcv9rsS1Ky7z\noygLPHjg3K5nL17CLw9c0OCH79/ErVsu4y9NU1S+M8GNE9ig5xHk2rTceu2XZHrqS+lY8LoMhZoj\no9NzBu4sb7hTZCWctpgMwBhbOmthY/M81ofODf3Ms6/jre/8K/fs05E/oW5euIYic6fSO7ffwujE\ntXtv7zauveg0pLbPX8OcXO2ddg+bHdcfOzffxPGR8wJcvfgyhs87XZygbXGw74IQ88kdoKAMH5Re\nAwjcYG3TnfA6/Q6UcKe6bquHp555Zan2Ac6rUtF8jnqt3ZrVn60wrtMaGPen0sVTdxhESInemCYJ\nAspUCVoBOAWQBpYhoNOt4TUFYrSBVhQMnSkwcgWorICsaCbG/YnZGAP9GCUs8txgXro2Ms0QllVd\nJcBQd87zBb01lqJF3o5eN4amA5stLapDeJpNUKoq4DvAeFplndYiepYLMO6+kGtHMQFALoCTCY39\nGWAoCDpVJchZh1Rb2PHyWVKWcyeMBqczVP0tGHxyB0ddA1FI6f8OwRBQjS4Rd3GWaDNzdhMTCjQ+\nGE9xeuKopsl06j2GRam8DlcQB5iVVVIBBxTdXzHwnHSbjEJGyRX7yqArl0sk0FrjhLL2Tk5OcOWS\n89xaMIREczAAsmpTFHqB2Fa752vnFbn2a8dsOoa1lMG5uYZ18h6fTKbgVE9NyBApeQSyrPBegOl0\nhtnMzfswDGCpD0LBIagcyGg0wplt50VaxkN1//DQB0DPJofYIu/YM+c2fPKNFHVW9u7Oh3VWVym9\n95gL6b0PURx6ocjV1VX/W0EQeG9qnqc+yxOoNbGuPnUVUYu8UUHos6xLrXzWrxDCB3kvg9/83f/A\nZ2grVWJG2YvPfuELWN9y3v6j40N8+JETWT462sPdA5eleP1mgqhD2XbzBMe7juI0sxSU34CX2n2c\no+SO3E4R0Wa4pi3UyaRqILrEoLPTOfrUD4dW4z5liR8poE+1QLP8aQSt5b02x/u7fo0sVQlDqdCC\nAwEtqhErsUolzlTYgVFVnUzjhbbXNtaxseX21CSZYJS7cSg6MQJKJJAygCBh3bKss/SFkP59GWN9\n6TGgTvyiH3T/w7gvS7MM/lbETD1pLFVV2WpRxPwh4so+LHdQx0lZf51eKG7sVNUX6L/HeJYWt9g+\n51IuB0OGvQO3uOzvpygo08XKGCskaPf65/s+C2dnZwc7t3cAALO0gI/NYAyV59+C+UXICrGQGsoW\nqCzreWXGOOxPkHZgjHljinO+tBG7cabljbMo2sJK27mJx/s7yBO3sG9euIwooEy6Bx9i56ZTNM91\nic3zLq6qLCQ0bQRieogPbrjCnXdu/ABnqIhqa7gB2SaKJJ+jSzE4JStxn4rKymgLEVEkP/qrP8H6\nxhpdn2Ow5hbK1177KoJoSekHAC1Rq1+HMkBeVAarQEiigTKQtQHKBaLITWQhpI8PscagS1mbIpTg\nxO9rlF7cMJABCoohSrLUj0EhhI+70Ko2pkQgEdBCwRkHo43PZPmyYW8AgAfHJfKkqmNo0e9TxmJb\no03xIWUmK3seAWc4pnilKORewNOUBsQ0gwcMouU2Bc0lZgVlEBnrY0tkYBCFFG/FgTa58seZwaxw\nbZml2s+/aW6hlOtbNc29DMMysC5Y0v0fxrA4DapFlTHuM4IY4xC04XLU1RQE56gYQhnWKfbDlT7m\n2y5bdzJNcDJyG9PpySkmYzI2lPIZiDySmNABwrA6e0pYoKjU08GQLBmnMZ8nXvag3+9BLWzIsorD\n09pnC2dFjgEJVCf/e2sAACAASURBVDLmqEHArXdxq4rb5OBEb45Oxz7mj4kWQM+blyWmtOEXRYHD\nw0P6rkRMBwZYi5iegXOOhJ6z02n5ubUMVocdDLbdnN5cGaLfqrKXHf0KuLW+ijtM0wzzSZUWLz0N\nF3AJUVkXDCiSOryiWk+LogQjozIIAp8dq7X2zxwEAcKqnp0xMJUgsjbQZDxKBr+ZL4N3P76BfYrp\nnM1mPrt0b28Xaer67fDwEJ/cdIfJslCY0WE5mShYitvrc6CduXe9FcR+fcomJcY9tycdqhlk4sZJ\nHxIlGYwWDLoq1hi0YEkuotNu4Zk1N8Zf3DiLHoVaJEqCpcvHhcURR1jV9Qu7/n2FQYA5xb7pskSP\njLUHtz9BkVQhKRKVqZJNR7hLMYAQwodItLtdRPS+GOee5gYAUAUFGYQ+VtVo5ddLxpmPmXLhCTRf\nBfMxWcugofkaNGjQoEGDBg2eAH8rPFOL9N+/M5jaO8Xcj9PfFosin1X184cD0OG9OdY+nufs2jMD\nCOFO2zc+voXTMWWxBB1osl2N4oDP9tA4R0GGW5sbOH/eudWvf/A+7t8n3Shd05ccC/VNsPD5QgmK\nT1N4i1b64uf/NtTq/fvvYHvLCWMmsxEkZZ6M5g9wa+d9AMCrX3oVbEiu/MhinriTRxBtQmjnkj54\n8D7imDJz7t7A/n3naYLQmOeky9PdgqXg0PnsPpLEZRbd3zuCFa6f2oOziCLnKRBlufA+NT66/n3X\nf0WB4eoVasHfe2QbO62WrwAfh1HtpeQS4HT6jOqsPWMZCnpOrbUXYU3nOWLr2sulqK9ROarcr0Ir\nlJVLlcGLXhpr/Ik5kLIWmuXMlx+CBQJJtG0QIcbyp6jxxFRNQZYbWMr4FADGVGamKHK0iELYzzOY\n0o3rVd7CvHAnYGuYr8fXDVuYTtx3Uy6gK29LqeoMxMxiyivvkkGbAsqtYtBVuRLFwEVVv4+jJM/j\nhdUAyXz585+1D4/7imbgC3puixm0sNZPLcGFv14I4d8F5xwCFXVk0CKBxV4cY50oo3R7G/Op8ygc\nj8c4psyo8XiGhCgWpVooaFwNTYRuVdrHaEzMcif+dhzj6uVL1D4gm7l5dnoUYmPL6clZAxjytlgu\nkGTk7dQZLL0TGYQ4nMzpc+DKWTd3VVFgTnpJXAbodtw8Ox2PcHxKOm+zKRIqk9QbrMKQnltnOESn\n57xg6WSOKKrEXw0eowoJXrx41mt5DXoDzz5ksylEdR/OfdBwtx/4JBHBBYLK47CQSZQkc6+TFQSB\np/AYY/4+eZb4z2UQIK6oeGNgaJwWFp6es7A+u61Upaeclql4+nv/1e/5wP2yLL0nMU0L2KIuOeS9\nrNai8oNwBIiq0INOCxdpDG6Ao0VexVavC7HpvHuRXUF54sajPZkg7gx8WwoSzuuub8JQoDYuX4Q5\n49bavWmO8YTmqC58duwyWFtb8bU6OeMQQd2fvPK0g/natJPjPZyQy5tz4dPwGGNg9I6YEJD0XcGF\n738OgBH1HEcRBK9p82ouSBn6+Q0YVM5Sl0hCtoLRyCu3+xL4uRU6xqdpuMfY0P/GlfZvfr5YSHmR\n9Fuk7QwWqb2Hs/kevv3ysz8tMty8VSm0tiBiR4NZVi78sEGltGytRkkTRnCOixecyNzq+hquUx2h\nDz64gYLiK8AYjKkHcd1vwgvmsYXUe8YZGKnN/rQ4tU///bPw8bsf4ON3HHe/1ulguOqWi9P5Hj56\n/68BAM88/WXkieP05+kxWl3nuh2sPIeAJkKR7+P2LXefyWyKgDKIJuMT9IZuIxU2hS6dwaWLER7s\n3gUAhJ0NXHzKySSkReoXnzBiYHAGSz4fwZCq7a1P3sNVsVztQcCpJVdibZYxgNL9udT++S1jKMng\nMkXhYzCstZC0kMZxiEr3vChKcMrTj3gAVIuwtV55mFsGtUDzebVeIb2Bpoz2dCEXwtdANNz6a5ZB\nxDiilvutQmtoQ7FLKcN4RjRcyJBTbbCcS/RIBFDp1I+vRGn046o4s0EypbEcAD2Kf7BaYkrUQjdm\nPmV+njO0iyrLK4KhMV4UHJurZNApwBB9fX6N40guv4CXpfJUqRDCUy9ccD8V9YIo6yLdzVEbrYsZ\nvda67DQAENBQ1Tw2DJKygwaDlq/3dvbcWczoHR2ejrFP1Pbx6BQ2cZ+fa68htpUgbYkHJEj7KEgp\nEFJcVyAFojbFs2QzjE9dRltRaKSZE75lTKDToRghxr0ivAIQdVxM2HQ8xpgyqk6OT72I7OXLl33N\n0d29PS9SO2hFWCVqBjxCSDIZ3eHAZTICsBIoS6qPVqSPdTjdHg58sWKmFCQdHtpx29sWhS5RlSNn\nIoCiw6PKFZLCPXMsA8REA5WldqUEQCEdlYo5555Cj3t9n/0ZBNIfSK3RPmRElaWv+co49/uEsgZF\nWsdbPQovPH/Vz4ksz3zM1DRJfYZuWZbIsuowpmDpc86s38SDYYitS249vjxYxdPn6ZD+zCXMWiSU\nykOcUFxVMp0goDEzzxUsGSBBHPtDaWd7G0d0uNW3djHNKTRAWy8Lsgxq0SE3/6rzoDHGZ1nmaYJ7\nO65gvNUGLZLfcEYt3YFxiGr9YxYM1VooFzIxuc+MF1LUdoC1mNNYAqvX2rzMvCQN4/XBKVgIo1kG\nDc3XoEGDBg0aNGjwBGCfhZBmgwYNGjRo0KDB/1/ReKYaNGjQoEGDBg2eAI0x1aBBgwYNGjRo8ARo\njKkGDRo0aNCgQYMnQGNMNWjQoEGDBg0aPAEaY6pBgwYNGjRo0OAJ0BhTDRo0aNCgQYMGT4DGmGrQ\noEGDBg0aNHgCNMZUgwYNGjRo0KDBE6Axpho0aNCgQYMGDZ4AjTHVoEGDBg0aNGjwBGiMqQYNGjRo\n0KBBgydAY0w1aNCgQYMGDRo8ARpjqkGDBg0aNGjQ4AnQGFMNGjRo0KBBgwZPgMaYatCgQYMGDRo0\neALIz/LHfv8P/rkVQrj/Y4GyLAEASivs3d8FANx863vonl8FAHAZoCwMACCQEfrDIQAgbrUghHt0\nLiQkdzYhA4Oxhm6vYY37W2vAWOs+txZaud8t8hRZoQAAw+EmVlfXAQDGaMgwcvfnDGVZAAD+yX/2\nj9ij2vi//te/aPXkE/fMAYPgrr2tQOAg6wMA/vd/zTCeB+63mMDvvpYAAP7jX7FodVxb/upWF3/w\n3QsAgLRMEQchAOD1qwX+m394BAA480yOd+69DAD46w+/jo0z7pl/9Wv/GivDGwCAd37867iz92vu\n4bjGF1/9DgBga/0vcZpsud9687cxnbpn+8e/8fmf2cZffiayvRX3LMM+sL7Sce2LBxgnJ/Q7EmHQ\ncu1TRyhmqWtHysGCHgBA6xw5vYfSWli6v7QCqnR9MElz2ID+RTKwwD1aliuMTt01WSEQhu4dnl8b\nIBauX2/dOcbhxF1zmigY696DNeUj3+H/8D/9zzZJ3DtRSqHdbgMAhBCYz+fuPtZiMBi4R5PSf66U\nQhRF1EYNKWmcco7pdOp/g9OYNcZg0O/Ts1mcHB0DAOI49mNZlQqrq25OKGuwd7jvfpcLhNK1NxQC\nksbaf//f/bePbOOHubFKuX7jjAHVN6z111hYVC+G4+FbMsb85Xbhb7ZwmWH1farrYeu56O5TX0/N\nhTUWnPH6u/TbnAGCvnCtLR7Zxna7bavvggHU5bC2fh5ttJ+jWmv/XuxiYxhDELhrgoCh03Z/bwy6\n6Mau//OyxP6hGzNH4xyKOo7bum+11osdCO67wfput9bC0vVpkv3MNpbz+/Z07H6z1No9MzWW0e/b\nhesZAMbdLYWo/0UwgWpdNtZC62qt1DDVGmo5wNznUhh/vZAMqqh+i4EJdz0XGuW8amoAGbl+YkJB\n0to96F195Dt84/Vn7cFoAgAImIT1z2ZAjwZjDBi9Ny5DnD13FgCwtbkBpd1Fh6MxxuOx75OM5qvU\nCqZ080AZ4/cVxgwUrU+BFKheChcMQViNFwVVuvuvra8gzdy7MCUHtNsz3v7o/iPbeOHKS7b6XSmF\n/5wxhjB0a22/30cQuD5USoGaDhtKcPqukAEk7ROMcQhZvWuGmKaLVTmMcs/Wi0JI6757kivMjftt\nZiS4cteXNoW2mvrc+rmryhLV+vHDb/3pI9uYFtbOc9ef1hQoc7cnjGdTKOPu3w1ilHNad3UOw6rF\nh6PaIZjGwjiHf++MAboaEIAfn5xZ/+6MEbC2pC8aWEtrsDV+PWBgC+M8QLvt9quLFy8+so2fqTGV\nZfP6oa3FPHUDep5mOD09BQAURYE0ywEAnW4IQYOAcYZ6PTZAPZz8osc4A7fVYOSgvgLnFtq4F88Y\nh65WVWahjPutMIz9RDLWwNLKXioLtbgIPgJxO4Yu3X2igKOaG5EEYlYtyAKBpA2XMQShmyRClhC8\nevEGllZhYwWMcc9cKAOt3Ms2mcZs5J5/d/8EBS0c6ewIw55bOI5P93D7wT3XxkBhPrvvHmi4izJ1\n1+8+uIvZrEst+PzPbF+314OiyTibFthad/eQQQlLfSxEB3G0CQCwUYRsfofaKiHgDBCGA/i5oiUs\nqsU5gJGuv3mQAyH1X8T9OwzaAoz643SiwGhXms1myCxtbrlFkVfvnD28qzwCSim/ieR57o0ppZRf\n0ICFw4BSflyHYeg/j6IIWZb5azsdZ3hmWebvL4Tw/SDAvLGQzucIItd4GQU4Hp9SZzF0u+5d6VLB\nqKqvQm8ILIOD0RxF7sYO59xZKgA0LKrdf9HGEvZhg6r6LW2A3FT9ofzBA9b6+QfGFw429aYvgxCc\nfleVpd/EI84QsspwLlAU7p5SCLTjGABw7YWzj2wjY6xeG+DXVGeg8er/MP88nHPfYMG5NwzCQHjj\nRAggpEnd4goduDHWkkB3242TQAJ7Y/fei8J4wxCc+cXfGO2NAWdJVX3LsOxgHc2m+PDWTQDAeDrz\nz4iFd2iM9e1jnPv1hRmFgAz9OAz9JjnPUvTJuB+sdGG0a998OsM0cdeUNkevT5v8oA01cuN9mqaw\nfff32kob0yNqa6bR3aTWMYN++4y7f+/qI9t4594R9o7d2O/EbeS5Gwsaph6DSsMY92zDtXUo7g4k\nd/aOfZ8EUmBv9wEAIC9KGFrTAxaiFbh+UFr79UMUc4TVVhK1oZV7n9YUYPT+GTjK0t1/NFHIC3dY\nioMWmF1+z3Dvy1D/CL+WcM69ccUYENI+wTmDrpwDQoALMiQ5IGgx4cwi8J/XxrWx1h+YtGUA7Uki\nYBDuVcNY+PVAQvq+Ukr5A0FZln4NWwa5ZZjQ/J5Np9j95AMAQJHP/RyNRYB05A7kxuR+NPNAoqye\noTQPrXNs4dBVGVMM9Z7KOSC5+9sajjSb0U0tKmJOKwXunTz12hCGMbY23Tpz8eLFR7axofkaNGjQ\noEGDBg2eAJ+pZ6rfG/i/tSohiJIJog5mE+f2k0GIbtdd1+31/EkKjEPSCcJ9Rpa5LlGa6hRQW6fW\n2tqzo60/YXHGoHXldSqhKms5mS24wDlCsn6ljBAG7aXbGMUBdO7u0woEJJmrkbSIjHv+MIwhspCe\nWEESfSUDQJL73RrjXOtw7klNnhhtuT9is8KinLu2Hx6PYYzrQzWbgdEJbno6we7egXueyCCvqDg9\nhs6cp+Tw4BDzpKagfha4lMjIo2GNRpIm1G6JkDwpWVZg+5kXAQCvf/lL+MPf/18AALNsD8a6ZyzK\nHLbysBkBTSe8kpcorXt2y2r3KxjoyASAWxCThkBaVI7DPCuRkacGjCOo+hUM2ix/UtRKeRe2lNJ7\nRoIg8NRenufIqR8AeG9Rp9PBZOJoifl8jjRNqY01NRLHMWLysORZjmzurrFFiUHPuZWZlEjIFV5o\nhYwoB84Y2uRRaLfbzjUE1zfWLOfRAIDv/Pi2p7s5F94jJll9vhKC+5MfFxzMu865Pw1bY2C1u4/W\nxrvjOef+BMkF9/NSaw1BnweBRuVB0Vp570IQMH8i11r7JnJYjOfZ0m0EZwt0ZN03nAt/OrfaoKio\nIwBxy/VtryOx1nXjeaMfIyKab5xkSMnj2RIMnZDWDGZR0Ni7MJTohI7mPp4WUDSPk1yhqIah/jRr\n4HlW76l8FA6OjvDdN38AAHiwvw9ROdw5YG3VrxamGvsWkNUaZ7WnwMCkp1g5Z/iVr74GAOiunUE1\nHEK9j7/85jsAgJEpsbHpxm+/H6NfuraO0xnMppsrWytbmB3Q+jsbQ6y6Z0gzg6cvPQcAuHL+lx7Z\nRssZghZ58Y1Fp/LKGu3HYFGUCCP3PFHcAqexI4TAvZ1bAIA21+jS+sRyhbDlrm+tnMUKvXOpU8RD\n50ILlcJwxYV9HOQan3x83X1Xz2GJwdCaYRg5L15eFCitYwMubm9DLvsSATBmYb0nS0IseJQseazy\nIgNjdahKNRcDHtTXM0CAPNXMIPAeHAOrK68NICq6UBvkhmgvKSFFRX0zWD9kjH82oxU07aNalw/N\nqUdhnCnsjZyH8d7ODbz/3W+7f9A5YvL826xAMXVrJ7Ol945BchTk7S9L+5AnWVKYgzIGStVrfI/W\nUTCDPK/2/gAFcc9Scu8ALorCh2MYazytKUSIl19yTM0bb/ziI9v4mRpT7XbfLxnGKJTKveCWtRiR\nKzcM69io/mCIQFRuTu754IqOc/epXY1aa5TEfxtrvEtSQaGsOAdr/OJSliUqLkIbDUEvJo47ntqR\nMoSxn174fjpkIMEDd88wYAhYZZSZhfiZEJwRNw/jJ4MU8Asi6F8BwEJAUwCKAfMxJCgs8rlr72iS\nIAyIs58rIHHXzEcZTg7H9DwK2cxdg6xEPnXfPTw4gVHhUu0rdInSuAWTC40kcZtbv9f3cSXHJyOs\nrq4BAL7whV/FN/7FHwIA7t67A4Tuu4oZT7sYU8CQkzTLNFS1ILQAo93nKrEoaB9VVgOCruHSU7tM\nLbjgJaBUtYEvT50AQFbk3vBZjFvI8gyntCAwMB8bxTlHTtR0mqbeEAukREkHAM64f/9JkngDjQFo\nVXEOlvuYkNIUnl42sD7+SPCaMuOc+XGkS+UPDMtgfW0DdsHA5PQyQjDohUWpoqU0d89RtbcyshgU\nhKnnB1uIM1r8exGLNAZb+LcqRsxweJ+5WDDKLB6Ot3ocOKq3oiusP1Bprbyh1G8LnFt3hsGVMz2s\n993fHPCGXtINcDJ1A7HTaqEbu+/22hIh0dBKWyQUSzNJ5hjN3bs+nkscJhUtlIOGCbRWdQiDtVjW\nmjo5HePDGx8CAD7a2fEGcbcboSC6Nc9LGFUZjhbUVLSEQK6rWBiGdsttPlla4uxlt/ZFmwmGHTKa\n+Agffvg2AGB3XmCTKM0LF2O0cvfdXAHHJy6erxwzdBP33WfPbWA8detOuM1xXKwu1T4ACCOONtw8\nE5ajTbGsk9EETFeHVoaA4oMiCejUUTk8DLAydIefZ194FWXp2jsaj7Gx4eJFZSvGJWcPQR3cxKWr\nlwEAOud49tUvAwC+89EOdnacMRW3Qm9MhWEXX3jtDQDApcuXcP397wEA7ty6gxkdqJaBm0r0zpkB\nq2L4jIJV1Vy3SLNqkxeIZEyfA4JRXDHnkJzCLgQHo8+VMd4IgrXgsqI1DUxF6VsNWR08GENOa0Be\nJD7OiDPmqUNmBVS5/HqTFnPcvuco6fff/j4+eOdN1xZuEZFhq5IMZeLendWlDxNwQT1ETSrj1ycp\nBSIyotO88AYX5wJDsiGYAHKKi1alhbFVHBw8/Z5lmY/jU0b5WC0hIqyubCzdxobma9CgQYMGDRo0\neAJ8pp6pSIqF06d0ofkAuDHYXHcuVfbiC2AdCrwVAUI6tVvGEYXOCpUy8KddY8xDmXqSMruYRU3n\nlSW0rjIJNBR5BQQXyOi00usO0eut0JNygAKildKPFbwcBAJGEl0hjKcppWCI6FTIuPQBb8JKiMp7\nxevAXizQlAzcswJcm5qiyCWy1LUrmSVIYndi1jMDTNw12SzDZEyuUzaHmjrqCFOG4tR5U46OjxCi\nDqz+WcjzzHuFZGy9Nw8mhLKU6cYy3Nxxp7Sj/a/iwtZlAMAP3/xrGEOBuQKQoWtrqUsf1BtyDh+Z\nK4CMMoXmUwU6eMByBoopBLMMrKz6Bghb5MVgasH7sBBJvQQEBAYDR4HGcYTpzJ2WWmGMOHYncmPL\nhYBQgTFl6gkhEVQcpLFYGQzpoeETLiQXPnDcGgNO4zpsBTg4OnRf5QAP6QQcCMgqO9LWlO94mkLY\nOsA2rNwOS+DVy+v1abX2dT5Eg1vAB4jbBQqv+o67ps74s1hIBrTW97ll7KHu9wHRjC0mD3ovswHz\n88BN8zpbzD7G8Y9zXmejLQTPGm3RjlzfXj3bxTNnnWelUBYt7q45t9JBQBM2TTLcP3GelTRXoCGJ\nsx2JHq1h7bANSV7CtY5ASt6ou8wiyat3rRDTmI+iEIzTwC04aobW1PTGI5Cmc5ycOtp+b38fIXnE\nZbCCGXmgx5Okpmy0WfBMBQB5rMIwxDOXrgAA7u/u4e3v/wgAcJzdxucvU7buxGCu3XrRtUBI7e6v\nC+y+PwIAHO5Osa9cssn6yhqsdP16HGi0Ntx3r3x+BeF8tlT7AKATtZETN8qkQZ66tUxagyh288xo\njaIKN5AWvR55emHRXXVr+uWnn8LxxM2/L/6dr2M+dX9PD2+gnbuA9fnRMU5T53lud/t44Xd+EwAw\n4gH+9J+7uZ5M5vjSL33R9dWDu3jnusuOfvraBt74BUcJXb1yHn/2Z3+2dBs5X/Sc18yJy5+oQiG0\nn4sArxkwZn1ISiSZp/kENELy4iW5Rs3ec+9tBmfgrAq8zlHlx2ptwKolmDNwXrMWfu5a+DVsGWT5\nBG99/1sAgJ0f/DUO7jgvlWiFnq41WYHZyYh+x/gsP0jhvf0cxredMe7DJaazDFnh5lMcRTjYd+oA\nvWEXW2dcEPn+3j6SuVunrckREd2ZJIlPLNLWgNMcDWQbxyeHS7fxMzWm8jz1OXjaukUNcK61eeo2\n2ZPxGCstZ1hJGdSUHhee6+W8NsoY5+ALxpTwKfC1YSIW9xguvPHCuICkB2p3O964S9MR0rxKS89R\nVvwSvvbINkrBoXhFY9STgXHu3bFc1NQFNHOp6XDZUnWK9MLfYD670Gpdpy6XHFnmBlk6T5AkbsFS\nUwNMyE07yzGZOJqPYQozo7ZMgezYGVanx2N0w+VoPgN4GoILgFHc2zzXUBXdwwTu3H0XAHCwfwsX\nL70EAAjkCgrmFi6jUnAKReORuxcA9NoMJWWEp4p5u0rBopq6pbKI6aWqAigTN4n6XQlVpe5qC16l\n4xjraaylkOcQmihfLqDIdT4apwjoIaKYo5jN6fYMK32S7Wh3cHLqFoRCFeiRMdVpt3F87NqeJAla\nLTKOjEFABqkQEmsbzq0ctyKcjimzBQpra442La3EyYlbEKQQkJWRLRiMWDR2fjaGkiOhxbA0tYRD\nIAWqm1q7YNwbAc6rjCMLVc0zy8D9rGb+AADGfnJMxUJmpbW13WBsTaUZ1HILn2II8RhhYbDWoqQF\nVitVZxFKjuHAbTQXN0I8e8YtyKNJDkvxFVIrzBOaQ2WGdUl0eqnx4303h7YChctt9x7vnpxiRFPr\nK8+fQSdwv3uhx7DnhgNmuUYU0BoAg1xVRiVqak8v0PiPAOfw6ftpmsJSanuWzX08n8u6ovlqLQI/\nDwQE/T3s9tClQ8L5rQ0w5QyTyftjfH/HjfEHhxNPe75y0eAWrTX6pERg3DWxyRHmbhyd7s4gnyXZ\nmS/P0W4TTVootKNkqfYBjppMyAiSkUXoJRk4MopHZLxeQ2E1QjqE9PsraLXc/CvzU7z6ORfHySxw\ndNtttoe3PsJ87DZMcXyEcMsZj+eeOo/RyFGWRaHAaB+azHK8/NIrAIAvfunz+OM/cSEMf/iH/xfa\nRDmtra6iFS+3ngKO5q33Kr4QM8V/Sswf6sxNq33cpDIaLZLqOLe9hRnRW/N07vc8g3r+cc79fSQ3\nKMjQ4CVHr+XoUcUCKDp0larO4OOc+djHZXD31ofYeY9o4hsfwCgan4i9cVdME+QZxY9yDkW/FXKB\nhA6iAbd+EeCAD1XQSgP+bwZFe20yM0gSZ9S32xEmU7empmkCS4feosjAeZ3tmM/p74j7+bUMGpqv\nQYMGDRo0aNDgCfCZeqYs4/7k6rxL5M7U1mtLjQ6OMVh1lqQxygtrcSZqD47gYJVGhAEEd82w1gJk\nSVprwStvDjcoyO2uTenFzCwKbyHvHx8imTkXcp7NocrqZJejLCvP1O89so1ciIcoCq+rxRcE85zL\nitrFvfuWsfoEYgyrRQyt8Z9bC/95UVqk5JnKsgxF5tpuEg1Qcl46LjAjmkqYEdS09kwlp67/p6ME\nor1cMCEXHNrzNwHyonreDJw7Sz8QPX8yvvHJA1y96II0uyurmKTuZNCFrJhUmFD4bJMgAEIKOi/T\nWrAxjLkfL6aoM550ZlBJG8k4Rmnqk0SbAkszbcGK5T1T733/257mGwz73l2uFXCUVoG9KU5IG20y\nnSFqU5aRrVnKXJUQsjol972rWmtd6xkJUWfMcY6NjXXqW42Cxl0gOY77bk504xgFCQ7m87z2UgYS\n82pgLIFv/cs/xZAokO3z56AD18YsaNWCd1wAC1Rz5ZkC19Ckx8OYAK98hgviq4uB5WwhGJ0zVnuX\nLIdZPHnT5wa1RtUCy0cB6Mu/R2O0p/bc7avEEAFLA2h3XEAy5ym5MJBot9z9B+trmJHn9uPjDNf6\nlAhRWOxPXT98V6UwwvXbUZZjbeM8AOCd+0CfqPCWUogppXd7INCjgO6NVoEPDt14GCc5yioJFfW8\nfxSGKz2/LuRZDqtIgPE4QUFacHbB/cetwdmVAf0d+czIuNvFeOTcZ71uG+u0/haFwmjqvNocIZhy\n/TGbM0xG7oFnPMOFp91Ek2EB88B5gj744BSXX3Ptm+kU5dT108AKiDqp+5EQQmJlSCEgAdAi6pVZ\n5t+tlBJBKcQz3AAAIABJREFUlekthNedszyErfiqYoYb77rMx1i20Gdu/N7YvYdk5DzGQ27RW3Ee\n4PZwFcf0+d7eKboDcqOrNRyRaO7Xv/413LvjMh/v3buLQZcSFhjH9taZpduoVOHXCWNkTclZ7gVl\npRRec1EIDlGliZtaWDUKGLpEX7/83NP47o8cOxBw5r361ix4uRgDpzU1QIErl7cBAJNJhqNTyp5T\nqMMBtPFJLlYrsMdYb45ufQRz6LyBpshQVGshNEKi6rKshKaAe0hbJ4cpXWeqygBSV1ppxq8NcRSD\nMQrfEUC36+559epTELS2zdMcCSUnTJMZSmJShAxrEd+ygGGVVuH/x957BFmaZedh372/f96kd5Vl\nuqq62k+Pa8wQBIYgEAEjgiIgQRJdSAxpo2BoIa20VGjFCHGl0EILcSHRgCQ4pCRQgoQZDAjMdPdM\nu6nuMl0mqyr9y3ze/P5eLc7576uhFOpXMRG9yrvoyMh+le8315xzvvN9X/Icy/KLx5caTE0n03l/\nkxCIuQQfJTlSVr2W2sF4RgexVYpNqQ9xal6AEMKIt41nQ3Nw28gLDTJMoxARP7goDhEntBGkSWxY\nCGkWQjEDJ00z6ELIzXaIDgba/F9EDFEKMRfpg5pDYvK5oGmuDfgzhw7/hp6PkuZQ1loZ2rtWBTsN\nSBIKqIrrTzmYSkKNdEYPIplpI87oqBAq5FLoxMJ0yP1IkxAVezH8JMsSeCWmITseZsy+kHKGUkDf\nWanUMGaphTt3PsetG78CAGitLGF6+DkAoN5wMeUTJJsCOQuTKyGKliyoNJ8HlLkDPh8ATRs9AEQx\nzIGc5Clc3mwbdReQLPYoFCbDxRf+N1+/hesvXQcArK1vwGNKdZalOD05AQCcd7t49JBw/4OjY5ye\n0cZ7etJBXMwvpWEzU6Vj20bAM8/znxH/hIGCBT7hz5R8BwGrRtcrZWyu0+aclX0EvKk6uTB9Op3z\nMe7sPVn4Hu//9CdYadMhdfvffB+ZR8HgO7/57xrRRiHmcg7APEqUUsHGPPguXoslJYSBWTNzKFDA\nWDCFAP0z7L+54rhJGIRmSh/+X71uc7Tji4OqNElMK4GUAstN2lRvLNk4Cunfl12JOwzbSWVjqU73\n/nA/wkmPfr/R3sBnR+QocDKIUGtRNJBogT95RPN8qVrCOKM58LQzxNYSvfd1P8H+jJ5bSQLbbXrO\nVmmG5TpN9A/2cjzrFRIX+cI9UzlSRBE9/WicIhG8JsIENks2lMqugXAbnoNv3yLxwYNOhg8ePaH7\niFLUl7i309bIi8NEWFjfpODiqnBwfE5B5+PzBGmBy4cexiOGSzyB1WUKKGZdBYf31mF3As37UfsN\nG46z+H5ardawvkXMu3K1DGnRHleplozoKISAbRWwszR9QLnKIHmeri835gd4GOMn36P+nfD4BEsc\nBGmZ4/iUA6uVc9z6RVpzcmkLf/r9d+kz5RJuf0xMtL2H96mHFMBsNkZSpYCrVqvicL+/8D0qnZvk\nMM1iKF2wvgWEoL3HcedBlmVLI5ng2g4Enw0WFF65+RIAILAFhj1yykhzCWkXyuhzwU9IGHHRViXA\nV1+7AQA4PDzHdEySEmGmDYz4vAMB0QgXf486joGEoeE8Q85/x3J8k0hEOUyPsSO1EaeGmgdu0gJ8\nTm6jMIbm+/JLZdR5n67USlhepnn7N//6XzeyGWfnPTx+ug8A+PCjj3HEIq6nRwfINc2NJMsBLs54\nnmPkmBYZFzDfxbgYF+NiXIyLcTEuxs8xvtwGdO2ZCouQMNFplE0xYZn3UXQOTLgs7UlIMde7KGCS\nbvcET/YoU/T8EsqM59hIkRSw3VnXiBLq5yxnPMdHwM2Wlm2bCoGAA3BG4FgBLOORtHhDKMAMpcJH\n6Dk2BrT6GbuH5+tAJiOHNpm9Us9n4XiuMf25xv1MIy1+znNTcUsSIGJYK4oVsqTQCUmQMzMmiYDJ\nlO1nwhlUBQsNz5HwWZBQ5xbCaVEyjhBHan5d7O20v39sKkfr66s4PmcfprpCOCgqhAqcxCLOBTEW\nAUBqQxCIE4005u9yxbxB2VFG+2QWxpAWZyqeRCmgf9xqWjjtLN65vLm6jpssOnrp0hXT0JqkM2xx\nhWg8HmJ1ifRy7ty7h50JQTynnTNMWIRzPI3QWKKG8mq1Zvz7VJ7D5wb0er2Osy6Jqk5nI1NG920L\nAWfelzY38OrLLwMAVtfW4Hv0bwPbNVDHk4MD/KN/8c8Xvsdv/tKv4eMfE+xxcNRHa4smgLQdk5kp\nzIVAxfMsMy0Rsbie73iQnPmlydyWQVr2z4gMzsecWalyDc0Z4fOVKUgBWVCL/q3XNv9bX8xcVLlG\nrULr+9JSgOvLdF9rFYHlCc2NmbKRMlPvYGwjzGgfejqcYMQp83Q0QJfbEKRdNnMMWW5goUazgtGI\nqlStqsDVZfrMkm/hyWN6VoNYGHbqTr2KakrfO56VMOL9bzBdfL/pnA4wHnLFPUrn7yoFytxgryHQ\noOmCnXoVSxXa+4bDIVaZ+arCISYRVdvqnos6Q5G2lWLK/nq5kNhs02dWWsD5gL73aWeGx59Qht9s\nevCr3MDdspGFBBHGoYbWLPQrtGEdLjIGgyGePKMKi7At5MwYrlR91Bj6ns1m6PVYjFhL1CpL/JkS\nkpiuwUoSNOsF7h9jekRrru7YcOW8Kt7t0zscD6d4+AmJlDav3EKDoaj7tz8D1gkeP3j2zAhFpkmM\nKrPQW60GBuwDuNgQKBaF0sJUqQSkqeJmaQ7LL/xoJWze865du4zj/acAAEenePOtNwEAJdfC7i5V\nIc/HIQoPRwVizwMEk1ncFnF55wqWWwTRxrMYVRYyHSQx8sLPTom5/50G8AKQu8B87aZZBl2I/ua5\n0RiUlpwTrQQAbiuwHBstJvJsbK3Dd+hdjKYJmqubAICtrU1sbpDgquc7OD+n97t5aQfVMs2T9a1t\n3HyVvGxvvPoKbn9GMOjHH36Aj1j8NhfSsBpd130hIeQvNZgajLqIIppkYTjCLKSJO5sOMGTvsVD2\nMTukhXF0eN8cFnk+lwoAFNKkECFLYDHQUKnUkDMLK5mdm+BLCBghNBuA8riUKBwzIYQ1V0RN08wo\nZgsx78laaGiLDh7Q2SOLcrgQ5vfklFSsGI3ii4W2jOVghhwKHAwKjZyZOlpkyPgFx7kyTJ0YqWE2\npGmMCYtzR3FmnpVr5ybYnMUZxmHRBxJDZYstDFvapjfLdW04LB6nZWQg0+FgCtfnHp9aC40mBR3r\nW6vwHnF/m4jnVHitjcp1lGtw7AK/amM24fcwzU0ZVdrP9c85MMJzcQpIZvZJLbDUpn/RaPtQYnHs\n++DoFGvrtBhLQQtBicU57dxABRraQMqzcIpur8u/zxHFtOGfn3cMrO1a0qiLO4FPCrwApM4x6tO/\nPTh8ip0t2hzqtQqarPa83KhjiUXoypUmUu4pS5TGeYeYSMP+ACXriwOMYlQ3ruHWO3S4vPltG36F\nDkHb9Y2St37Ox0vlGTTPZc+x8KM/Jur3Wr2C196mDfz99z/AlZtvAACW1rcAFH8nNXChhoY0gZBl\n5gDBgJwYJLlhOEopzVpUWiM1EPoX32ut7OJt7udpWDl2OGGwXQuvrdPffzz1sdehgya3PVxbK/wz\nc3xyTN/biWJc2qFgueFW0A/Zz9MPIPkwWq74uLpBcNT9J0/RYQmSG+tVfP0qHbLfvzfCo1PuTfJW\nsdmgQ3kjnmFzTPc+DkeGkfpFI45ypCGLFCdqzqm0hOk1DWyB19YomhI50JnQXFtvKaQp9cgo18az\nDgUsg9EM77xFFP+D41Occy9VP7HByx6ZBFJFz29rtYmxy/B1lOGEr2e51kDMzOHhUwtXbtL8rVQC\nJPHiazFLJTJVwLAaQYnFGKXEZDb3liwEg1WiEPIZ016rI+fAN09n1NAIYDYYwWY9FU8CPvcZtVeX\nUGbI7/HJGSoMCQ27Pby2QUHzHdtFxmzjLJNzGZFMIEt4b1MOHHtxNp96LjARFEUAACy4kLy/0sop\ngpEEFkO68eQMgp0vNrY3oTgwTITEV77xNgDg9uePcPIxiY46tgOPWzp0HKJepuD67TffwCpDY5Yd\nGNX+2SefYXZCgX6eADmL0SKXPyPu+0VD57lZx1oKWChkQXJkfLblSQiLA6hKvYW11W0AwNVrV3D1\nGv985RJ6XZqrk1Bh5zoFR9VaFeUSzcnTzgn6U5oDsyg0yWeWz4W5d69chuCWEL8UoNuj+OPO7c9M\nlJHn+XPyMV88LmC+i3ExLsbFuBgX42JcjJ9jfKmVqQ/e/z+NN85oPHhOvE+ahvJ4NkHOsJTtBQYS\nWFpum0ZEL/CRcNl9f+8IEWeK08kIjkeRdqO+gjCijFNIhZBF3YSQUIU1i5ZwWCfJ9cuGOeHaLlwW\nUtRCI2Hti0WGlLZpopNCm6Zz27KNloXOlfGT08gNtCeFNgm3Fi7ynLOtTBlrkTxLoDgrzPMEgrMk\nMRNIWAtnFqeII65YJSGynDNvkRo/rihRCKOiEgAsrIaopNEdcQIbFkM8QdWGYMHDUR8Ai4BeuXoD\nbW50znQGL6CMTaUSWaHlFcdGIyTVgOPTPQV1FwEKm5YcAWdvyhGm8T6HQMiZrsh0IRWGZqOE3Obf\nOwKuu3je0Bn0cHx6AADQeYL1dcrgG606ItYhmoaJ+TkIKqhWmC2a58gZbo1qoWGV1Opl49MHkRv0\nyraB3S0Slas4EmtrBCM6lmX86cZhgsMzysaUG6DFz3M6HqMzoN/3xkMsnicCk8kIpTpVD7UQyAt2\nUJo9x0adVyuVUkg4K82SFD5n3sOjp3gk2AJiGhndq8k0ghAFvJyapmCyOeHMWzpzq5jnqjFaa8Sg\naoTtOKYKmSmF1JBBvC+8x29ca8JjKGvVVWjxu8jcElSh65NKeAxrxZmFcpWy2If3TxHx+qsEDspc\n4dAS+NrNqwCAW7deQcoZ8N2Hj3HvCQlWDkYxPh/SM7natrFcoWutlhwMGaq+vddBr8UsI9hICu0c\nS0IUjKYvHM/BsHpe2ZNSFh4l8CXwxg5VHU8GKY5YzPedXYlxygy1oIW9YyKGjMMclRpVzBqRhn3K\nYrSeD9snmG867ptqd6xTeDx3xiJCxvv4amsHvUO6v/4DCyd839Uy4K8tDp1ICdTqvGeIBAHDT+fd\n7lz3TOZQ3NIh1HzP7XQOELG/Zc2xEBUs2MkEVd6rLClIuwjUwnDlCkFj8UEXe08JPtuqeaiyAOlO\ns4x9FhRLIMD6z/CtikFRkjSD4y4mggz8LMSttDbN1uq5yrAUGmCG+WAwMNf8k6PHBvI7fPYYH33w\nYwBAo9GA5IpbZzCEtOldry4tQ8V0Huwst1HhdoNH9+/heL/Ef7+P0Zjmb+fwKWZcYXSsEjybqzwQ\niJIXqEypuYVTpnIgL6q7JTRa1ApRb1SwtU3739WXbmB3l0hAzUYNjSbN4WYlwJAr+bZUWGOx71hl\niJjWHcWxaeuxyDfG/FxAipaQaDB0eOnSLr7y1lcBAA/vPYbmueQ4zguRz77UYGpn52VEEUMg3TPE\nES3UXCXQbICrZilsFjzzyzXYXJJ8+Y1dXHmJNjHLtqH4xZSCBk4YMx6NR6b06JXKsLi853s++n2i\nswalullUdqWMgMUqm60tVOocrLmekTHQOsegu7gKqmVJgwdb1hzms6Q0QZZWKXKGapTO50GlrVAA\ntjp3ITVN4qozglumz9SrIabsjSdEjkqZ7vflzRGCMpUqlYgRsZFyEifmEMwlABZbzFILMZfkswyL\nsxa0NKVYjdzITLRaDXg+l6HjKfoDFtKs1XHG+PV77/85Yg581tfewCssfvfZnXu491mBWc9glbns\n66Rwy/ScaokAODiaZNr47iklTWCaJ4CtC/qwjwH7BkbpDCpdvOx+3jvDhA/JsWsj4c0n1zvwWKTx\n5OQUh4dE9Z3NQpRLtNjTNMW1qwRr1uoNnLJQZynwTT/U/v4+Wi36TKNehc8slGa9jmq5bK6jgMbi\nNMWDR8QcPDw+wY0btMmcn3dxfEzXkCSJMaBeZJTVGGenrCZd9qGYQu56HspcCq/ZAikHFFOdA4o2\n21z5uMk9XNbQx+2P6N1tf+07sHy6/ihLYBcei3lu+h/SXCNnaMxCCnD/jFLaqLlDaQOPCksa6jQ5\n+C4OZbqeZdwOdnaX0CjTHH80dPDoiOCrWaYRcXJSdRRkVohzavNvJ8LF5/uHAIBvvPEavvEL36TL\ncSSezbgnx/dx1KPnGSYxtpcJU1Rxgi4zvq5UBKLCazKO8fiUkx9YODhjKEXLxXs058xwlgCYB1NF\nbDzLJHoJ3d/1a038+Ud0jQ/3PVy+ytIoeQk7O5fo94+PcMgeajtbGzjvE0w6HkxQYfNY37ZxzPIA\nUZJjPGV/SGXDljQHn5304Xt00G0gw+kn9LwfPjzB6n/86mL3ByDLQ+S8D3plG8Mhfa8UKQJmyiol\noLkVw/YtOLynW7YNmdPvfWFjOmCx21zD4/3OdyQsPmCnowmODug9b6/v4pxVtDsnB2hWKZB8/XId\n1hk9w5NxiFTRNThwkKqiB26I9tIL6D9AzfVUVGbgSIgUeeFDbCmUWIQ4KNmocFLaqlWh8sKndC65\no1SGBr+vo6MDhAndezPwMOvRfqxG51A8xz/Kc9Sq9K67vZ6B31d3rmJzjZLJwSjC+YDOTmm7gFw8\nKBaYSwM12y20WTqisbqLpS1iIK5utnH9Kn2XTlP0uryeegOAmc2WFnALGQwIdA8Jih1lGSxOmKMw\nNAzWXOXGwUJhPk/iLMXn9+4BAI73j9FlFuetG7ewv0/zn2C+xVngFzDfxbgYF+NiXIyLcTEuxs8x\nvtTK1KXd66YpfPtSjIRLsEme4PiIYJXDu7dRWaGMZnV9F1lOEfXqRs0IaOW5MA2H2+sbsLnh99PR\nGaSgyoFXqmDEzt1ZplEqUyWg20sRs/6U41pImL1RLtchm5t8PTF0weQZj3B6eLjwPT6fVYrnNKes\n5xrQtc6g/z+88ISQ0Jx5W2IC36FK01/82ga2Nujaqp7COKNK2SgCmrtUqvz3b3jYaLJ2jjrAuFvI\n5mvkETdGWgmELoQjBRgdRZYp4zD+RcMS2jA6EjVFk7OZkltF4HPp3Bkj4kZ3iBQnp6RZcnp2B+0W\nMS5++6/9V3iNS6un52f413/4XQDAH//ff4AwfQIAUHmMMOTSMAxyAR0rOJy9WVobq4fQyU0z+mgQ\nIRkWApuWEfBcZKRZgoMDgmysXGHIzJzHz/awvknvYW/vsfl9tVpFmStK/X7f6EltbmzAYU2UyXCE\n1VWqfI6HIzgFqSFXxjtxY3UNKZeqx+OxcT53XddAhEdHR0aEVQiBMXsClkolAxEuMibPPsPWJlUj\nDroHOO48AQBIkSHga/j2tR1UapQpPh2liFz6OfVX4XDpY2NlCXe49D+zKrAY9sgxhV0I9GZAUSrJ\nhTCVRJWEyDNuyncceJzb6UzNUWetTUVP2jZeBMv86bMRrrHek3J8POwwzCMFWEoJl+oBMhZi3aim\niCP6gpvLLu5z5WgaxQhZl+r9Tz7DvT3aqyAUzs65MiUlIq5ANUsSN5fZWqQiCnQGS0tV7Hfo3YXl\nGvIRfe9wmhMcAUBEz/uofcHQ5j8kwMg0MMexIHlNJND44BFVhdqtJl5iyO/7H3Zx8zVqOK5VbNTW\nv0Wfj3+A25+QjlI6fIizY/awSz2kOTc6r1+HE1Dl7ez8GE8Z0gxDaXzczoZTNOs0Ly6tbeLp57SX\nPXwwRP94cYsOlQNxoUenM1Pdd10LDlfxk0xA8AtVSiKZ0edXV9q4vEqwXTjs4SlX3Cq2DZsnoaXm\n2klZHKPLhA7hBqa6F1suzng/9Rt1/O5f/SUAwP2TE/zkE6pulEtVHB6zRly58iJEN6pKFf0JuYJh\nIQkJzTpZ1XKAb3+VKvmBnWFjhd6d5we4e5euYW19DT5bpEhpobFEcO2g38Xdz58AAJaqPuwS7RM6\nnsKxK+bzhXVKrbyOVov+/tvf+BYm3I5x5+FT/PlPbtNlCmm0mRYaWiPga3v7m9+Au0ooUz/zMOE1\nF/VirO/SeywJgYzZ9b7nostzTMYxPK5UpyrGObdjeO1l1Gp0L2k8w2RA67LX7xr2/mgc4uyM3uMs\nifHeu+Qf+2d/8iO4Nl1bKfBNG4vI1AI0l/n4UoOpg2ePEfCh45eqqNQoaHJcH47LePzhISSX9CBt\nGEFrDQPtuE5ufh8OzjEZ0oEyGk+gcvbwscrGt85yXDjmbybwPFolk9EQDQ4GsmyGs5O79HnbhV2U\nA2cR+i8A82noueq5EKYPyxICluG8zj+jNeaQBgQsfn31Uo4r69z3YLn4v37wAACw3crR+gr30kQJ\n3r1NJduZrfB3fo/EMZcv72BYoQDGr38OaBKadMXYrNM0FZgWgp8KMP9jgZEyVh5GCivLzJKUFmLu\nS0vT1LBZ0jSC5fAGWPJw/WUKoG6+/DXojP7tUmsdv/d7fwcA8NK16/jf//U/AADcufse0oT7caQC\nKzLACdWcEaYtMJkFcWbN+xayBBGLvg0GQPICrIxWq4GEBeb6gy6SAu6JJjju0LOUQqLCbLs0TQ1U\ne/nyZXz88UcAgGazhV/5Dvk5vvvuu8b0eHVpGRm7NldLZVQYrnjzjdfx3nvvmesoxDN7vZ6R8Gg2\nmyawKpVKaDZpw/Q8D573xX1Exfj0B3+Eq2+QevP9oz0cHJLUyFK9go0mwQMrb66hUWWhvVGKMKK5\nNrESNHkBVm0HTok+nyqN6ZT90qwIcaGeLlzMDwgNaczBLAgWHVVaIi2CL2tuBJ7mGXRcwIWAEotD\nC6f9CCWGER93Rjjs0r0stXz80lcIKq1I4JVtOuhFJjGa0F7yth/jxjZd2+FQ4ckx9w75VWxfukbX\nlvVRuG8LleDW63RArJdS7LTpXXgQaPB7bK2s4vvvk4nwySjBkzNaL6eD2OwB0NrAIYuN+V4jrEIt\n24HgSKDqSgSs0n5nr4tr21fovmsZjs7pnfzFl9sYKQqs3/rKm3jIkHucJFAZBQiP9g7QanGPlSOw\nuXELALDlX8KAlcKjaIKIe6bCJIbD11Pyb2KL2ZDjWCEevQil3kWa0J6e5jk8hrocKZFxz6ctfcxm\nbPg+DVH1aaOwch+NMgUFbhqjwuvDynIUEX2WKKQ8RzzLQcxaEMlkgBL3jtnLm6ZHLM0lnj2g4HTz\n2g0crtPnj4+PkSru74z/bTmQLxhaw+L1YWnAKsRxlTb9gpfWt7Fap31iqeJghWHEs9EEde7Jc0QO\nwS0gSZziZJ8CinrJR4vZupvLLYTcZ2m75bkjiWXNAyvLQpvbEKwkwvSc7rfsOcZMfRrGmIaLeyxC\nAYFDZ0KjuYxPj+lvjrQPmz1FnVjj7ITmUgUKYzajzvIchZu9ndpwi6KEcGA7BcSZgX2vIQEjAWRZ\nGpqZj71uBw/u0Tk6DkN0+/R8jjtdw9rzXYGA51ijVIYtFg+nLmC+i3ExLsbFuBgX42JcjJ9jfKmV\nqTQJITljEo5jhBelY8HhMpvIbSRcdv/8/idwuOl49/qrkF6hiySQ5yzjPxpiytYleZ4b65QwnSLJ\n5lmbH7Ar+vouuufUWJimMXxm7wzHA4AbCKEtCFE0N+bGumSRYQnbNIVa0JAMn2UyM5m3n0dwVcF0\nCs29jJIyDrmsftorY3ONIKW7j49x+wmVJ8djiRX2iapWJDZ3KdP88d0B/tX3qIz9N/7Dd2CX6d/+\n5m/UkUoqZ/paGI2RSZJgMGORzziDvaBqpxSWEQ2Nohya9UK8AJjN5o2QHgurTUZDlBhjc/11XH6J\nNIlEBoy4ogjbgsNZ4ze+9stYXSZ22//yj/8H/OjdfwUAcEoxiiw8zzWmo+KKNDRb5GSRgM9zxPGA\nkREuTbBWmjd2f9HwPMeIxLmWg4x9bEr+XIPJdTzU+TNhGKJUondy5coVHDIsrJVCo04Z5M7WtrE9\n2lhbR8xQoOd5aLFYXr1eNyyg7e1twyJM0xTLy8R4EUIYeNFxHKSFltrCd0cj7R/gR39MxI3EV4hC\nbqqd+GgHVGEJAge2TZWmzbaEN6I5+Ph0D02utgxnNlES+SLiGVUSPSeD5qZzJTJjP6JUCqdgu8KG\nLGyS9Lxaaz0Hj2utDaMmSRJk1uIZv1bA6YSu/5OHQ6TMyKvVUjSZ6eTIDGtLpGFj2Q5mvCZsKFQa\nNGc6gzGcJa463fgG4hFVqk8PHiHjPWZldRWXd+h9xaf3cPiYoJfOURcZV1Ez6eHWG6TDldx+gMqA\n5pU3VZj1i4qeKZq80BBSGE3VTAM1fiUlX2LCoovDszF8rg688/oGwoTm4MqKh94ewdpuuYxKm2Cg\n6toKWoKqka2RROsK7Y/O8jOkIe0vGxuXsMdCoOIsh+D3k+YCA274vnPvKa5fInh/1OtieF4YEH3x\nyJUy+02cJEBO3yWVABgCk0JgPGIIN8xQkSzKDAdPntFatHWEgnzm6ByCiTM5bExjbo8IMtQaVGUt\n+2U0mOXplMvwWN/q/uMzfP6MqictWcHKOlmwnJ1PcXWHkBbXdfAilf4kDmFz20eWp0gYboslcHmb\n9sK3Xr2OdpXe3ZXNZWjuTB9ZEqttqiL5gW+a0Vd2tnB4RPcejQdgSSVUAhdxl1s90pjokiCNw8Kv\nUqkMEa9jWyhYDOc5EqY157w3Qpff7yJDAUgZ+7z/6CkO+aj1Gm2EDI8ncYgB2y3Bd8w6iOPEwJ2x\nZxs423c8LLcYWbKl8QhVuYLgipLvOnSfAHQSIWSx4TRNjThtreoiZhTLL9vGxiZV2QvBtV9qMPW8\nqaslbdN7M5qMEZqDPcKAmVSZtFFbYm8iy8JOizaiSTTG6YDKda50n1NaVnOqtXy+Ryk3k8yCRIUX\nf5JJjEZFqTLA6vouACDOU4P9W3kId1IYHX/xENJFzljvNE6Qssp4Co1ZTi/ytZcirAz5vmzAK9Hm\n8qwI/IX/AAAgAElEQVQf4HBMi3nz+ls4YIZBu13FV19lI90kwe0Dup5aSePKNbr33Uur6JwXAVeE\nMpcqd3er+N3fIObV8PwQ5Rr3ByDC0jotjJcun6MkF7tHz3Mgw4JplUOB/oZlpwhKLBRYstFnX7Pj\noz24vIFf3v0qVpYZIgkTRPxc/XIVCfdFRJMMGyukPv6f/Sf/NVptCnZ/8MPfN6JvfhmYTJjRCBhz\n2jQUaLfoABR2hCjm0rALQ9NfZNRKZVRYzC5PM9gc4LSX2ohCumYV56hwP9RSu4nVDTqA1rdW8Z1f\n+UUAwHgwQIUFYnc313DepXlte56RwBBSoc5MId8NcPXyZQDA1tYa2qywvrmxZDzYOsdnmPS5F1Ao\n+MwuLAUBPHvx5dxaLcHm0nbmZqhE9NzCzMeQg75ZmsEv+gfkDM0xbc7XOz1UWzcBAD866WMW0vPR\nszEUM3Slkxpm13imkAQcnPqeIeRpLeHKwqBWIjVSKcTaAUgtXvLPWgpkpcUp55aUCLkf46CbQkua\nA6/v1vH0mAKikm9D8D3Waw7GXTqUyyUXjav0LqrLu7CWCQYrlSycjei+vv3Lf8UwDR3fQzwm6ELH\nE0QTgnqPDp6huURz4+DJ5+glvK84LrJ87v8pDItXv0BgLEwjYRA4WG7QHtGNZ6hwUpFrjVNmYKVJ\nAsTUY7LccFBhdXihT5HNKLB+9jBBmY1hleXBZvHdla1llG/SWvftBN5p0RwXYzylPTRTgMNYfNkX\nKDO7qtM9R7VE73Y4TRGFi0eLO5e2kIN6Dc/65xB5IRyroIrDU1kI6pRsuK6LNsPmK8tNjDjR7u7P\nwNsBXA1or0jMFLglCGEGvHSV9srd17+JGQuc5jo2ZsuX7AaOU2qhWNncQsywWq3WQJ3h7lLZx3Se\n7X3hiMMZ6hysubaD1iqt+92dDXzzawTFL9cquHGFgv4rmytGTqd8fo4PPqS5trO9jWfPKCj2XNcw\ngzfX17DCSuF5kqDJvZg6jSF4b3Zdl+YHCFYrEr+ffPATZOwPOM5t02qTJgpZuvh7zLXGjIMUO8nM\n/loqWci5kOLWKsZjMRcCYNmiVAloDo4UJHTBfJQKLmN7jqWNKLLKgW5/wN88ZwC3l9dw6QrdY5pn\nZg/unJ2iw4xzLRSG/Ulx0UjU4vd4AfNdjItxMS7GxbgYF+Ni/BzjS61MZVmGpw9I1n406iPm5sYk\ni2HbzAqLIiytUyZSaW0Y93Pb8tFk7yOtUoxZg0KlQMrRoxQaiqNT27HguIU4J7luA8A0OkOhgh+G\nLmyLrRaEwmxC0aklcwguKwblJVSq7cXvUXroxxQtx0lCNjUAfHcVVdaK+gvflkhUIRbaRokhh3pg\n4+qblH1U2ls4OKDsOc9yNBjmQZ4hK5qytUTGeawXBEZbIwgcZMzIivMZltYow24sLSONiwpgjNff\noow1aHTR3dtf6P40YnACBr8sAEFZaZQMYdncjCunxLwCMBweYzamisxf+82/hXXWLEmnM6DwTkxy\nyELnSriYjGhelMtt/O2/+V8CAKqNBv7gu/8IAKDUFNIurIIkfIZSRR1oNul5n41yBNxY+vZXmkji\nyUL3BwD1Sg0OZ2xZNr+2bq+PkMXsrm5um/fWXmrgpVsEA8ESKM0o5WnW1tBuErzhuj6kRRlzohRy\nnl+WC6zyfG9UG0hTyqTbKzU0WlSx0jrCkz1qGt3Z3MCUIY2P7txGyCXszfU1NEoLGiwCOM2GKMBr\nW2q4HmelWYoRw0/jQRetwkctz1FY0jVWAigW1u09O0W5tkvXmYXQIf0+m41gJZTZP3twgMo1srZY\n3bkM9Zx7vM9ZKYQwzeWWbaEgl6oohmatFzcoFcbziw0LQE7rYxwnqDNMMpxmOGW7l1XlQlQZhksU\nBM/bzK5C+dSAXGttY8TNtrmTYOcqVU5LjRYsZriFvWcIB9TIPjw/gmSNu8bKGjbYMyyXwOghtRiM\nekNMooJtmhlbKCkkxILWR5YlzP4SOAIv7dAecUnEGPKDmswiI/6a5xY+P6Dr3V4f4bd/maoVSA/g\nFVYf3TE2rpMOVBiNUWdfs0hkOJ2Rl1nu2lht0d/Z2//c2CetLa8YKC1Pjo3lSaIUjvos/Oh5uHxp\neaH7A4ByqYzGMq2Dm6/eAFKuuCVjo13WaKwgZTRgFiWI+T04lkaD1+7R/UMorqRIS6Lwr1LIUJQo\nwszFowE9t+/90Z/BZmrqSrOCkkPrr+zU8a1f+At0cdU6/ul3/ykAYHB2irROny/PAnRZy2nRscYM\n9pevXcb2Oj2f5XbVwHOzyQB3PqO94eHdO0gYGhtGM/S4CjOdRlDMeO92u6YJvl5rIOej3pK28QX1\nqhXY3P5QrzcMgaVWq6LH7MWffvopLK5UOrkDVej4SRf2C1jmZFoj5YpSrVZGmUkC1WYVLm8snu0B\nXCUO45TeE0jcVxu71gw2N/oLbSFXhR2cnnvWxjmKZhuVa4AJGE7gYvsqaVqVq2U0uJK7e/06nuxT\nxXYaRkiYhPLD7/2JITQtMr7UYGo4OMcnH/0QAJCrFCXuV5KWRDSjTaYaO2gz/dzxPGMquvcow/79\nPwQATIZP0edganNlEzNWKE/T3EgTSCkMPOM4LnxWRi+Xm/C4dN3rTrC2ss6f1xBsxhmHU2hdqIxn\nEGq28D32RiOch3RoLm9cQoXNNculChoefa9wa6QCCwCWb0Q+AY1c0vdOkwRLK7zpKA23WFXKhmJW\nWwaFhMuuOo9gC+5jGTuwXGZmiBpmMR1qYZSCK/6YjGLYfHCXrRmmzmKTJtczcKsQGisWJC/e6WwA\nz2cPwCBFpUb3MZmdY3/vEQDg61//DtyY7nU06pqFANhwC28qz4XLQXAcRbAyWuy//Rt/F2FIP//B\nv/gHsHijU0qDiW4oNxVcj3tPFLDSpP/x0g2N0fgF4CHbMYrwluUiZnwgimI0mTJ8/dYNVOp0Peej\nHhSX+5dXVlBleOOjH3+ME6bC37j5GvpsmDjqnBqx03a9jVaLDu3PfvoxPIYfao0NhCzh0emcoMxl\nccexMWNT3K3NDUxCeqHDwQjjF+hhODg7NpB7tVGCzBjuzhSgaYLlOjd9C+k4hsWbcO4EuPfpE/o7\nD/ew+gu79HzSMXyeymWZQfLcXG/WjAK9mI5Nz4PrAD4LMkIpaJ4DUgOSJRNcBeScdCERL9CJQnuA\n4PWUZRlGU3pHvcEUv3CTnnmr5ED69JmgsY7WZWKdpQgwYjgqOj0wvZvSEtAM3WtHI4v5mY9PMT2m\nhCTsnsKz6DMbay1UGxQMzNIUl1ZojR6fDzHkfq4ky40JusbPKs///w3blqZPKsmUYXneuLqG9x4S\nbDcNM9QrtHe0ahVjtn0yFJCGQZYj4Wez3qyj1aQ+ndsHe7i8Q4Hgumdh74SDl1dipJoO8CcHIRzu\nj1xr1dEZ0Nz0HcfIsDiOy4rBwPpqG9dubC50fwDw+PFjtGcEe339na/htZep5zKwU9z/9F0AwMHD\nT00CsL/fRcbsT7fWQM7wab/bgc8Py5HKMMgsS0KgULr3kfKp/ZM791Dy2RFDCkxY9dxTNn73t1kG\nJc1xsM/nVmAhZXFkxw2wvb278D1KATx6QGzakpVDs7fgswdTZCwYLCEhVNHbVYbDAXK1VcXyCr2j\noFxGnc8bz3PhsRiwH1Rheey9KSQc7rfyXQu5UxQoNBTfu5QSFd7kr7z8MlKWnbjz8Ah//B4z3i3P\nmDAvMijoZ2mjWR++4KyoFxo/QbieEQLVlgUluBfP9c1+qV0LGjT3YpFhwvo+FizYzE7ff3qAnPdO\nX0SI2VxcxRkS7lXV+QhLdTr7mxULj/j3vdM+2tyTXK9UX0SX9ALmuxgX42JcjItxMS7Gxfh5xpcL\n80UxFDe2Ka2Rc+Zq2Y5x7vakhemEqk65dQqXyw627cLm6PTwLMKQxTa1PYRbpii6WkswHdHva7Um\n1tYIMnOcEtFkAEBLA4e5jo+ldo0/Y5vvyvOsUOVHlooXkpR/+PAewNDFZu0lKIuqUYfDAZ5x5ri2\ns4ZamapO09l0zqKAMk3WAmJe7te50XuJ4xRxQtlKmIyQcdQt8wR+ULBPVsnXEMD7776H0ZBK1Btr\ny/A5Sx0P+6g7bLfS72I4WrwsnWXP+Vpxw2ASzkzDv21ZULLQaQpx9w6JAL506RZGXHoOJ33Y3Lwr\nIE35WCKHy5CsJWwj6iiljd/9q3+bLkBl+IPv/kN6ftMuVhp0PVU/w4SbKCuRhGIWXqeTw3GChe/P\ntlxTCp9MzjEe0/N+88034fLcuff559hhH68oi/HJRx8DAFqNurEk+eC9nyDjMuL2xg6g6HmnUR8u\nkyA85AiHVFL/4Z/9AC3Wj0mSKaYszimlh2qFsvOn+8/gsj/Z0uqSeW6nZ13cZ8uZRYbIUqNhE/Y1\nFM8719LIeX51p8DKEq2PcNYBErrOydDCn79P93ueTbCUHgEA8v4YkrcUr+4i4Sbo+nIdtsMVw6yP\nnKt+jrbhuQWRQAA5N7KnMWReWMjYyBRrPOUKVrw4s1ZrAatgDyuysgGA1bpnGIW2VJBsgdNcXke1\nyT6SSmDIkO5k0oNd4fmTTKBY/ybPJ9BcOR+dHeF0nyqwWvrIxvSsaoEPsOWMFhbKJYZ6swxTxsTS\nLDfWQVmWL+wHZnkWCjPEUAmM2R5qMkpw3qW5lmllbKPqrQrKl6kZfjJN8OQRtRG8fG0Ftz/nKrXX\nRmfCjOKTYzQaVMHLswzDY3q3q6+EOD4lvTWFJWND4trKWC9ZIkbMlRrX9s38UjJHnTWMFhm2bWNj\nnSplv/arv4q333yH7nd0iN5jYik7lRwr63QNsx0HvQnry4kK7h6Q/Uzf0ZDcVuC5AqUSV8IdB+CW\ni9VagMoaa8HVHHhF1Ua4mIwZRgxjfO97f0LP1g8w4vMmjyQ078VJkmJre33hexRCGDHr/f1jbK/T\nO7q0vY0sorNQCgu2TXPHdXwj6FtrlNFuU7W83W4ZVjH5ynE1BxYmPAdUnps1qqUFyR60tjUPBZIk\nMRXM0+M9POQWkB9/fB9d1tmz3CZEujjmrgBoFnLuPL2PRsBsO6nhM+oiHIkC31fSgQa34FgeHGbj\nTzZa0BZdW3/mIjpnz13fgc1Q+Z3bn8Hh6lXefwbFyJXIBVyek+fPOlixiAk/nUT46b/5Pt3jhw+x\n+9IuANKIc+XiOlNfajDlWTa21inAsT0HHrMNKpUaAmaBBLaLEi9gv1xjming+AFGY8LCH+0l8Jlu\nX2muY22NNsCVYQef/pRE8TbWd3H5KtFWe72hMUMWEKiyUmqlVjUqt47jwuVrsKX1nAErkLzApNHZ\nFOMJTeKHjw8wntHL+7P3PsO0Ty++udbAX/7LvwYAuLSzAs3lT88rQYmC3RQYQTUh5qrI0hdwXbp3\nkVYLuzoELrB/SHTs7mmE732fDrsH9+6hwqJuzWYNt16mRRhYKc6ZDl8REW5dW1no/qKZABPaUKkC\nFfbOc4REypIWUaahFfdLpDk+/uRHAIA3br2NlSYHkb0jKJ6oQkj4laI3JyGdBQCO5c4FXFUOnwOi\nv/Ef/adotenv/Mvv/k/wQJDG+pKNDjOtUkciYZjm9DhHubR4vfbw4NCYMwthYYdL9i0+aAGi3WcZ\n+4HZtulRG/VCNHhee8LHhFX4j548QJ4VsGoEm8UNo65CNKSNYjaN0Wrzxr6yjZFHn5+MU6QxfVdr\nacX0ivhOAK3oGUa13PTpLDLqtQqKfaLT6SFlI9pyqYpBhw7/jz+4h/4xbUQr9Rw6pADhxz9+jLv7\nFEC1rzbQn1DpP0fVsH0ESpjFLPAXWwhimrOWUwLA4n21Fgp6e5LEgKG0K/igNeo4DpRDQU2aThCG\nL2KSK+dG47ZtYM2n3Qizj+lQ+JWbNexeI2aiTsaIWNiz1FzDKpurri7VjTyKSiJEI4LNhWfBa1NA\nLbPMqN1niTZwgisBB/SubQQAw44qSZAyfKy1Nmas8gUEO7Wl4TCDVgkStQSItj5hSNlzBapMnW80\nlgBmTx6ez3B4SuzMOKrhsEOfb5SnOByR6bHv5PAdZmSGEdyYrv3Bh328vEZ78XJjHVpwT4oTmXUA\nD4j5eip+gB6zpm17hvwFmLWwLFy9/hoAwIWD3hElDP/93/tvcXCXkrTLbR/OjOZm4EjsrlHwlfkt\nnHcokXwcZYYlqaWAw0mR9DwonrPNRglXr5EY6dXD2MBn1166hf/tD78HAPjpjz/B1ZepZ+7Xf+e3\n8Pf/u79Pf//eHpwNeg4nx6e4dnVx/8FUwez7R8MQ7915AgBot1/HJgeeQkjTV+c6LiIOsrxYI4ro\nvYzHlIQBQLlchu/SetKQCPgclZZrhKRnSYI+JwbnnTN0zyi4Pj44wsOHxJYPx12jEN9aWsG/8226\n92mYYDCtLnyPwnIRKzZkPjhFgyOPii1Q4cCq5Am4ReussHDao7nXm6SwWcV8abWBSlAEXBXELq1j\nJyiZvsbHD/q43uKA68GfglutYesy9IyLD70OVJmFQwczDDs0n5PZDLM+/WwHZWMivci4gPkuxsW4\nGBfjYlyMi3Exfo7xpVamdnYuocV+QZZtw2MoxbEdmLgun2vnjGcxPmINjfZSC2FEkaRKNSRnQ4Pe\nCNUyZbEba9s4O6ZIdTIZYcT+PLPJBBZnIs1221SmXMd9Dka0IQvNGymoJAUgVxq2Xrx5ebMZ4PCM\nrrN/+AS3H1HG1Dka4qvM+Drv9fH7//M/AQD87r/3q/DZ085zXWOvIS0Xmn8WmDea2rYFhzWkpEph\ncVUrzQGLm6Af3b2Pg8fU0Pj2q5ewuk4Z1oefPMTRI2It7CzZAGfMV2+uYbm22D2OhxlsZnhlMZAy\nzFAq1U1jdBymxnbA9SLsH1Hl6H/9w3+Ob379G3ztQ5TY30tLieUlvifPh3Iow/C8EpBSRmIHAQoF\nNdf28Du/8zcAAGtrS/juP6PsUItjrLSZfZSN4Dbono7OYsRFxrzA6I+mWOHs9pVbt9BmyxalMjMv\nHNtBwHYyk3CGjG2MoDXqNaqa1aoNPLh7HwDw6M7HWG5TSV3qyEDHw1EXp326tvPjU1xhEVYBD2OG\nirJUIPApvcpVDMWNvd1eFwcMY9zf20cvWvwebddGmM6FQy0urwdlD2Oea9NRFx90qHqxtd7A2gZV\nl+6cniDlhnirWsJgTBCx68ZIuPl3ID1kvKb7gwmOTljfJdUol+h5ri9vocxWQ3EcIagRXFH3S8gz\nelZe4MCxGLIapWRIueAooDOgqFLR9dzdH6B6md5dpFpwUtYEiixMuA3BtjIIhuu98hJSFiNKwgGm\nXYa4pn3UelSlKjkKvsUElmSEWpltTxzA4XucDcYGVrm+FqDGZIOhtqExZzIqtVhbgesBzSVm8JYl\npkVzbcWF5GbipeYqrl2+yQ8hQ5yxhl+u8OyUq1qphVqZ9tyz3gCThCoUX3l9C60Sp/WzMXYqdO17\nIxsxs2aXbKA74YZg7cEvE0wtl88RMASa79cxOaLPSBVjODtf6P4Aalz+zl8im6yVmo//8e/9NwCA\nT/78hxDM1Jz0pugv0XysuBZkh31ezz7H54+5iqhgRKIzCGgUMJMN7dGcbS03sbmzCwD4z/+LX8Xm\nJpERltrLePyE3vn7776HzV36zG/81q/j9//J7wMAOs/Ozb5crgb46te+vvA9QkhjOSmkZTTQfvLx\np9j6Dv2dWr32HBScGVgwKJdQZWHgSqUKze/99OwMkynryOUSowntqb3RGMMRE1u6PZye0f4RTqdw\nuBVmfXUN166R3dLWagvXr9GeVK1UsX9AkN+dO3fwq7deW/gWvVIVNvvjTtBFylWq/fMRJDNiA0sb\nfTTXstCbUgWzGyoj2P24+wxNttWplqdIIrp+1w1wzufZybiFZZttgc4ewHVobZ2Oy+iFTHrTIbIz\nWi/jXooRPyvhOMgK+7AseyE15C81mNre3UV/QAeEUsr0M1jSgs0bV54kRsbgwcPH+PD9HwMALl/b\nwKuvUgk2mqwhnNHkWFtq4ZgZFfFsgh32nnr06BGecA+JZdvYvbILAHBsgTSiB63TDA7j/ZaQhlGT\n5zm0KuAEuTC7BgAENCrMRht0TiBmNFHeuHkFa21a8I26jf5nJPx2/9NH+LW/RH0AtgyNkGmWK0Nz\ndQIfkoO+JJwgZXFAqRUUwwZhkqDPENfxcQ+bbIR5/fIqHIvV35fWMTglo8rqatMIp1lCI04WK72X\nSxaKhjKBFB6ztJQWiFlQT1gAx1JwHQsT7v36ySfv4c4Dolevrnh46zVajDelBwvF4dkyTJXE8yAD\nhjS9EoJqmb/LNX1v3/r2r6NWpbnz7g/+ITrHFETa+RClgjHX9DCYLY59Ty0Xj09pQ3NKe9hN6B2W\nHcvMEdgOJizMJx0PDlN98zjE02MKHifRCJUyXWfv+BnqFlPty7ZhnY6nQwxP6Ls2yhYc7isYdPuo\nMdytoQFWsc4zF9U6wY3d4SE6vEkmtoNyfXFphNkswiynTSOwfVgMvThejmtXKRisVBWmCUGN55Mc\n01PahOs76/jKNfJY3Ht2G50ObWgijyA5AUiSGhI2TUySCB7Tq7NwgpBlCZ7Ouijze3QcG35C5XVU\nahAhsz5ljkrAjLzQRR4uHjBKKU2vDgATsJQdgRvL9PyXAiBnkeBYlTDi/jWdJfDqtPnnykLO/Tbp\n6ByzPgUDQijEU7pm6UnkrLjvODYiDvpGZ+fGV7HSrCEI2MNxrYqGx24EqXyuL3Nx0c5y1cH6NgUs\nrWUPmtU/As/G5V0KBHa3r6LMSctp/ww1hgWvrVTR5QDn8NBCtUzr7KwfAdxvstGa4fIOXXvD9eEK\n6gPqPh1j7x57MzZjZCzG+Ohggpx74EobYyyt0d886OU4O6NnPLJinHUXZ0enSQKPN5OT/T3cfo/Y\n4DXXwRnDQDNLQvSZbZlOEaYUQMW5i4xhcNfSyHhPTzNlzJPrCrD4oA4qDppt2mPe+ZVfMj1K0DAG\nwhrKtN86XhlVXqPlagCbxYPXlpfRXm0sfI/PDwGY/fXO/ae4tkWJ8OtvvAYpCtmDnjFT97p9nA/p\neQohcH5Gc/O000F3xMEUHPRY6Lc3HJnnIIVCs0Lr77VXXsHb7NV5dfcy1tiU3bYtE7iNx2NM2U9V\nu1UElfrC92XZDip81jZbbYQzuv7cSjBmP89BOoNVnMGZguPxmVCpYpsD2Om4i5vXSUB379ED+Nza\nsL3ZhsO9Uf3IwyyledsfCswkXf+ffnaMOyfsQtEsoebRu5uGNWQ5LZ48T9DnfrHlZt3A74uMC5jv\nYlyMi3ExLsbFuBgX4+cYX2plqtFqwWc2i9aYs1Y0jJdOOB2bxtgwDBGz+JzvBbh+jZr6Ntdnhm1X\nLpdxxB5EvV4HRV3OdXzcvUvsmkajhmtXKZp1kEFzJqVVjizk0q/OTAVKWpYRJNMaC5fdASCVASYR\nZUYqz+FzU2q7uYbVDYr29w4eI2OJ+/fev4sZiz8FQY4pZ0xJqpAycUlYrmmetXUKi/3wkjjBpHBL\njxOMONqfhilusBWGtF0sr1KWWjqYYsS6K7lSKB70cDRGyV2smfDS5ZqR8680Q7icXU+nU2SFtUXV\nQ5qygCcsMuIDkCHFKOTmvqmLDz+jJvnJOMdX36DsebO+gj5n/pZKoBjODWotpBGzNnUTns9TV/q4\ndeubAIBWxcMP/vifAQAGvRBa0HO1RAhrQSFEALDrbcTMTHx0fI4JN4uvtZposWZQo1aDw9UWQKLK\n0KeVzhAOqFLz2kvb2G3RZx7euY3Tp1QiFyvLsHh+dTp9TLlJ/a03X8HlmyQqN8lmEG4xBwWG3MCb\nAjgbUgZ2OomhS3w9fgPTaPHG3k5niMRiFqRQxp6i2qrAzgtIKzRZ+HA6RJ8bdSNRR3uDqlefP9WY\nTZmhORggYbZMMAyhuNpVq5bhlphFej5BFBdN3hVIhugnsxzZiN67Wm1DJmwbIgRGA/63XQvuC3gs\nCqGNYKXSudljGkGOb1ymqtN6UwAhVWLCyRiDPs2ZSXeC5St0bVnexXmHGu51OIHDFVKdW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T98pxPVPqRmqBPMgvy7TpV5pK43gr5cRJcDweG2tQkkiTbDFV0qTfvxcy6SBhK0Z/BGiO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\npicLKGeor671bdy9Tevvwql5xD5BLJfevY3SJF0wzXwVOw0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JMuTRjpkkSHO5t6qqMJje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\nwP8DE43dRjhawIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8367518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072)\n"
     ]
    }
   ],
   "source": [
    "print(classifier.X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_one_loop(X_test)\n",
    "print(dists.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3803.92350081]]\n"
     ]
    }
   ],
   "source": [
    "print(dists[0:1,0:1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n",
      "[[ 3803.92350081]]\n"
     ]
    }
   ],
   "source": [
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)\n",
    "print(dists[0:1,0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BQIAnT56QTqeZmpoSRC+dTguCWCgUKJfLOBwO4QHt7u5KcqNaufV6ndPTU3Q6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\niYiIiCRSZ0xEREQkkTpjIiIiIol+A1rafnUl9xe+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa82e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3803.92350081  4210.59603857  5504.0544147  ...,  4007.64756434\n",
      "  4203.28086142  4354.20256764]\n",
      "[ 420 3684 4224 ..., 4375 2744 4601]\n",
      "[ 2641.43408019  2707.54796818  2753.57930701  2785.19819762  2806.03118301]\n",
      "[4 4 4 6 6]\n",
      "{4: 3, 6: 2}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_temp = dists[0,:]\n",
    "print(dis_temp)\n",
    "sortedDistIndicies = dis_temp.argsort()\n",
    "print(sortedDistIndicies)\n",
    "closet_ind = sortedDistIndicies[0:5]\n",
    "print(dis_temp[sortedDistIndicies[0:5]])\n",
    "print(y_train[closet_ind])\n",
    "set_label = set(y_train[closet_ind])\n",
    "y_pred_temp = {}\n",
    "for ii in set_label:\n",
    "     y_pred_temp[ii] = list(y_train[closet_ind]).count(ii) \n",
    "print(y_pred_temp)\n",
    "max(y_pred_temp,key=y_pred_temp.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "object too deep for desired array",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-103-c06cb40810a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Now implement the function predict_labels and run the code below:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# We use k = 1 (which is Nearest Neighbor).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0my_test_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdists\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Compute and print the fraction of correctly predicted examples\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\study\\cs231_cv\\assignment1\\cs231n\\classifiers\\k_nearest_neighbor.py\u001b[0m in \u001b[0;36mpredict_labels\u001b[1;34m(self, dists, k)\u001b[0m\n\u001b[0;32m    162\u001b[0m             \u001b[1;31m# label.                                                                #\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    163\u001b[0m             \u001b[1;31m#########################################################################\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 164\u001b[1;33m             \u001b[0my_pred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbincount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclosest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    165\u001b[0m \u001b[1;31m#            classCount= {}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[1;31m#            flag = 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: object too deep for desired array"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "print(y_test_pred)\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 2**\n",
    "We can also other distance metrics such as L1 distance.\n",
    "The performance of a Nearest Neighbor classifier that uses L1 distance will not change if (Select all that apply.):\n",
    "1. The data is preprocessed by subtracting the mean.\n",
    "2. The data is preprocessed by subtracting the mean and dividing by the standard deviation.\n",
    "3. The coordinate axes for the data are rotated.\n",
    "4. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (500,5000) (500,4000) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-b77b9d1f73c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# root of the squared sum of differences of all elements; in other words, reshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# the matrices into vectors and compute the Euclidean distance between them.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mdifference\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdists\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdists_one\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'fro'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Difference was: %f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdifference\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0.001\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (500,5000) (500,4000) "
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# Your code\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = np.zeros(num_folds)\n",
    "    for i in range(num_folds):\n",
    "        Xtr = np.array(X_train_folds[:i] + X_train_folds[i+1:])\n",
    "        ytr = np.array(y_train_folds[:i] + y_train_folds[i+1:])\n",
    "        Xte = np.array(X_train_folds[i])\n",
    "        yte = np.array(y_train_folds[i])     \n",
    "\n",
    "        Xtr = np.reshape(Xtr, (int(X_train.shape[0] * 4 / 5), -1))\n",
    "        ytr = np.reshape(ytr, (int(y_train.shape[0] * 4 / 5), -1))\n",
    "        Xte = np.reshape(Xte, (int(X_train.shape[0] / 5), -1))\n",
    "        yte = np.reshape(yte, (int(y_train.shape[0] / 5), -1))\n",
    "\n",
    "        classifier.train(Xtr, ytr)\n",
    "        yte_pred = classifier.predict(Xte, k)\n",
    "        yte_pred = np.reshape(yte_pred, (yte_pred.shape[0], -1))\n",
    "        num_correct = np.sum(yte_pred == yte)\n",
    "        accuracy = float(num_correct) / len(yte)\n",
    "        k_to_accuracies[k][i] = accuracy\n",
    "\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "3",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-66317692db12>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# plot the raw observations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mk_choices\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0maccuracies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mk_to_accuracies\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maccuracies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 3"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x83808d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 3**\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The training error of a 1-NN will always be better than that of 5-NN.\n",
    "2. The test error of a 1-NN will always be better than that of a 5-NN.\n",
    "3. The decision boundary of the k-NN classifier is linear.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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 },
 "nbformat": 4,
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